RL

D3AtHpAcIt0

the math problems serve as a benchmark, that they are capable of creating new ideas and combining them at a superhuman level. Realistically, all that RL training for these capablities made them far logically stronger as well. Also, all the major labs are absolutely using the current model to make the next one better

Echo-Possible

A huge chunk of both Claude and OpenAI tokens actually pass through Azure, AWS, and GCP. They have revenue share because the cloud hyperscalars host every model behind an API. Enterprises trust a model hosted by the Big 3 over OpenAI and Anthropic who may or may not steal your business by RL post training on data aggregated from your prompts. The value of the Big 3 clouds is the same for AI computing as it is for traditional cloud computing.

eht_amgine_enihcam

I don't see how the context window makes my statement about LLM's being a next token prediction machine incorrect. Yes, but an AI is not trying to say anything. It's trying to minimise it's loss function. It's solving a different problem to you. My other gripe is the space created by tokens maps to language, not necessarily reality and the artifacts related to this. It's a man in box who knows the word green is semantically close to tree, but he doesn't know what those two things are. At least bring up COT and iteration heads. I still disagree and think there needs to be RL/some world model.

Echo-Possible

I work in software as well. I completely disagree. Agentic coding has become insanely good with RL post training. Everyone in tech industry is adopting it. Go ahead and fight it if you want. Die on that hill.

Comfortable_Wrap4509

strict per-unit hardware gains are only one of the ways to scale llm capabilities. First, you can simply buy more gpus. If the old stuff isn’t much worse, you just use all of it. Second of all, RL and improving data/pretraining have been just as critical to getting them where they are today.

Greedyanda

1. Mistral hosts other open weights models, specifically GLM 5.3, which is currently the second most demanded on OpenRouter. 2. There are no better European open weights models, which is relevant for security reasons. A Chinese model could be tuned to send critical data oversees once detected, which you cannot rule out just by hosting it locally or even applying your own RL (though the latter does decrease the risk substantially). This isn't just a hypothetical, researchers have proven that you can engrain such behaviour into the weights. You might want to check your own cluelessness.

Partnikl

I would really like to know who these regards are in RL

softDisk-60

I imagine investing in trillions of i386 chips in the 80s wouldnt be a good investment long term. Or assuming that petrol cars will consume the same as they did in 1970s. I assume future models will be more optimized than LLM&RL ones.

ricetoseeyu

It’s loops all the way down… foundation model training, RL fine tuning, thinking, and swarms are all loops!

titanomachiatto

They modify the weights by filtering data, creating new RL environments, improving the algorithms and hyperparameters, and running the next training run which produces the next set of weights. You seriously didn’t think that the tens of thousands of experts controlling trillion dollar companies didn’t think of this? That you, oh genius redditor, had some fundamental insight about recursive self improvement that everyone else missed? 

_hiddenscout

My big thing with NKE is the China problem, which I don't think they can solve. It's been declining since like 2025. In 2020, China made up like 27% of the revenue, now it's down to like 16%. We seen On growing their marketshare there and then there has been a rise of local brands. I mean even Steph Curry signed with Li-Ning. I don't really do a ton of retail stocks, like companies that deal with fashion, usually have terrible luck in this area, outside of like RL. Just the China thing makes think there is no easy bounce back for Nike.

Anreall2000

They should find a way to stop open weight models becoming better. The moat of AI business, is stealing data and hardware prices, they should solve unrestricted distillation and forbid Chinese access to popular resources data, if you believe hardware price issue will be solved. Because Antropic doesn't have some impossible to copy technologies or brilliant established engineering team, like the og Google moat, as a matter of fact, many AI RL methods invented by Chinese.

fredandlunchbox

The competition keeps releasing frontier models that aren’t as good. They have some room.  And the training paradigm will likely change. They’ll start from foundational base models, then fine tune/RL on the same base as new techniques are discovered. So instead of $10B for a new model maybe its $2B. 

kinglavua91vn

Nonsense. Training data already ran out 2 years ago, why do people keep repeating the same thing over and over. Compare Opus 5.5 with any models around this time last year, it’s not even close. You really think the models got significantly better because they scanned a bunch of books ? The gains are coming from synthetic data and RL scaling and that is not stopping anytime soon. Every year people said they will hit a wall and every yeah they get better. It used to take 6 months to release a new model, now a new model comes out almost every month. If anything, it’s accelerating.

alteraltissimo

Yeah, it's quite funny how coding skill, in all models including the top frontier models, seems to be *way* ahead of the ability to write or make a coherent slide deck. Probably because the former can be RL'd on verifiable tasks. Unfortunately in many businesses, just taking incoherent claudeslop and passing it on to colleagues is increasingly the accepted norm so I'm not sure your point is valid anymore.

chadguy2

I think people blend together AI with genAI. DL, ML, RL, all of these are AI, and they are here to stay. The bubble is around Anthropic, OpenAI, NVDA and all other cock suckers that circular finance themselves. Their business model is unsustainable and they still have to compete with Chinese models which are on par in terms of performance and like 7x cheaper. People don't hate AI itself, but all of the hype around it and the fact that everyone tries to shove (gen)AI down your throat - look at our new AI feature, look at these agents performing as good as our senior employee (they don't), look at this AI phone, AI car, AI cup of coffee, AI this, AI that. LLMs and genAI is probably the most advanced and fascinating technology that we had in the past decade, that is ruined by corporate greed and the rush to be the first one to milk the most out of it. It could've been the most amazing tech that we have, but now it's absolute garbage, with Anthropic and OpenAI dick slapping each other with who comes up with a model that does 0.1% better on their made up benchmarks. Yeah cool, we'll soon have Fable v1929.49 vs Astra v1202.01, each costing 2000$ per hour of usage, with 9999T parameters, but I don't need it for my day to day tasks. I need a 1$ / hour model to automate and save time on the dull, redundant tasks, like your average Joe.

Anreall2000

I think your game will be automatically bought by open ai, decompiled, added to training data, updated some RL metrics and refunded in Steam. And we probably already fine with such approach

AntiqueRadish1218

I absolutely believe that AI will revolutionize the drug industry, but it's also true that current progress has primarily accelerated in domains where RL is most effective.

2020isgreat

The reason AI is so good at math and programming is because it's easily verifiable which leads to RL being a cheatcode hope this helps!

Medical-Lecture-6053

i would seriously slap you in RL if you said this to me you punk

asdfzzz2

2 years ago you had Phi-4 - https://arxiv.org/html/2412.08905v1 - "Synthetic data constitutes the bulk of the training data for phi-4 and is generated using a diverse array of techniques, including multi-agent prompting, self-revision workflows, and instruction reversal." Then major labs saw that it works and started to add their synthetic data to training loop too (1-2 years ago). Where you could verify the result (math, code) - they are pretty much superhuman now, running on mostly synthetic data. For baseline language models it is less pronounced, because you still have internet-scale datasets with real data and there is no reason to throw them away. Generating internet-scale synthetic data aint cheap either. RL step of the training uses a lot of synth data, though. And keep in mind that internet is full of synthetic data now (llm-generated texts are everywhere), and newer models are fine.

Vlisa

people who make money buying and selling consumer discretionaries like LULU, LEVI, and RL are smarter than I'll ever be. Don't understand how people value them besides "the brand ain't popular rn"

nagai

Wow incredible research, just a minor detail, harnesses != post-training / RL, I have no idea how you've managed to conflate those concepts

TalkingTajik

Here is an interesting gift article from Bloomberg comparing US luxury (Tapestry, RL) to the European side. Hermes is in it's own league, although I'd buy Tapestry over Kering or Prada. These brands, along with the handbag and fashion portion of LVMH, wax and wane in popularity. Hermes is the only one that successfully balances supply and demand--which is quite amazing when you realize the cost of making each of these bags. On the US side, id honestly just buy apple as an example of a luxury good (iPhones are basically a necessity for the most of America, but they will always produce higher prices models and accessories for upper income consumers) and then go with Hermes if you really want a leather goods/textile stock brand stock. https://www.bloomberg.com/opinion/articles/2025-03-17/ralph-lauren-and-coach-can-make-american-luxury-great-again?utm_source=website&utm_medium=share&utm_campaign=copy "Coach, meanwhile, released the New York range — including the Brooklyn and the Empire — last fall. Another blockbuster would provide a fresh avenue for growth. Its notable that Empire models costing around $700 and $900 have proved popular, potentially giving it scope to stretch its prices, although it must not make the same errors as its loftier peers. Sister brand Kate Spade’s sales are still falling. If Crevoiserat can follow the Coach playbook, she could finally fix the label. It also recently agreed to sell shoe brand Stuart Weitzman. Tapestry walked away from acquiring Capri Holdings Ltd. after a US judge blocked the deal, so we’ll never know if it could have worked its magic on Michael Kors, which continues to struggle. Right now, there are more pressing matters, such as meeting demand for some of its hottest bags, as my boyfriend pointed out. The viral Empire Carryall in a deep burgundy that he’s been eyeing for months is still sold out. When a Coach bag is as much of a must-have as an Hermes Birkin or The Row’s Margaux, it suggests US luxury can survive the coming bling battle."

2020isgreat

Pretty sure this new AI biotech stuff is all hype. Too few trials to be effective RL needs like infinite

Sufficient_Habit5091

As a bear, my concern is they won't truly let it crash and will keep bailing out AI even if it's shit. It's amazing even frontier models on high effort and token usage still routinely hallucinate basic information on simple questions. Just completely states falsehoods with high confidence. Hard to believe many of these public benchmarks are not being extremely gamed in RL environments. Not sure what a solid defense is, if it even exists. Cash may not work. All I can say is don't put all your eggs in the US and be diversified, hedged like a sane person with international and non-USD assets.

Oopsy_Daisy16

RL is down with the sector too but has solid revenue etc. 

Comfortable-Leg-5467

You may perceive intelligence as autonomy but they are distinct things, and ai researchers treat them as distinct things too. And yes what you’re saying about them not following rules is true, that is why RL and other post training is so difficult to get right. That doesn’t mean it’s impossible to get right. And it doesn’t mean that no matter how it is trained, it will inevitably want to kill everyone. But maybe that’s a hard stance you have, I won’t try too hard to convince you out of it. Anything u heard about hard boundaries getting violated is untrue, any amount of post training does not create hard boundaries. Only deterministic code can do that prior to routing to the model.

ASatyros

Jacob Coxon @hilbertspaess X.com I resigned from Anthropic today. I spent the last three years doing pretraining research at both OpenAl and Anthropic. Neither company is acting responsibly. They are racing straight to self-improving superintelligence and gambling with our lives. More thoughts below. 8:04 PM 9/8/26 78M Views 11K 118K 494K 182K Relevant ✓ View quotes > Jacob Coxon @hilbertspaess. 14h X Do not underestimate the power of this technology. These will soon be superhuman systems that can hack anything, revolutionize any field overnight, and acquire real power and resources. We have all witnessed the progress in each of these domains, and progress is not slowing. 376 75.8K 75K 6.6M Jacob Coxon @hilbertspaess. 14h The people building Al earnestly believe that it could kill us all by the end of the decade. This is not a marketing stunt. If anything, many executives and senior researchers will couch --- Jacob Coxon @hilbertspaess. 14h The people building Al earnestly believe that it could kill us all by the end of the decade. This is not a marketing stunt. If anything, many executives and senior researchers will couch their phrasing in the press to sound sensible but I hear the same people express fear privately. No other human activity poses this level of danger. 764 11K 75K 15M Jacob Coxon @hilbertspaess. 14h A common response is "if they truly believe this, why are they still building it?" At OpenAl, many have not deeply internalized the civilizational stakes. At Anthropic, the stakes are well-understood, but they are locked in a race to get there first - they believe no one else will act responsibly, so they must do it themselves, despite the risk. 219 73.8K 5.1M Jacob Coxon @hilbertspaess. 53K 14h Accepting this race and entering the "endgame" is a hubristic gamble that should not be launched from a private company's Slack. Attempting to speedrun alignment should require extraordinary confidence that there are no better trajectories available. 99 2.2K 40K 4.5M --- Jacob Coxon @hilbertspaess. 14h Accepting this race and entering the "endgame" is a hubristic gamble that should not be launched from a private company's Slack. Attempting to speedrun alignment should require extraordinary confidence that there are no better trajectories available. 99 2.2K 40K 4.5M Jacob Coxon @hilbertspaess. 14h I am optimistic about the potential for coordination. Warning shots like the Hugging Face attack have made pacing agreements between U.S. labs more viable. I don't feel like we're on track to prevent a global race, which may require costly actions such as a temporary ban on improving model capabilities. 110 1.8K 34K 4.1M 口↑ @hilbertspaess. 14h Jacob Coxon If you are a lab researcher, I urge you to consider what the next few years will actually feel like. Do you want to kick off a superintelligent RL run without a rigorous understanding of its mind? Should you put your head down because "it's happening anyway" - or take this moment to call for different conditions? 838 3.1K 48K 3.8M↑

Wenzel_Washington

I don’t get people saying competition to space companies like RL or spacex is a problem. There product is low orbit high quality data acquisition by satellites. Afaik that’s not what the rest of space industry is doing. If it prints money I don’t know though

socoolandawesome

Are you aware that the proof OpenAI came up with in their solution to the sub problem is very different from the one that tristan/levent came up with? And that the tristan/levent researchers never actually solved the millennium prize problem? The 2 researchers literally used codex/OpenAI models to come up with their solution, how can you then say the ingredients weren’t already in their training data. You have to explicitly overfit to benchmax on certain benchmarks and it’s typically done on RL with a bunch of different sample problems even beyond what’s included in the test/sample set for a given benchmark. There’s so many different things that can get in the way of perfect recall if you are not explicitly targeting something such as catastrophic forgetting or different post training or just overall data load. You are making a big leap saying you know this model forsure learned it from some anonymized data that may have made it into their training set and been processed in some way. And I can guarantee you that anthropic (who levent is an employee of) does the exact same kind of training with anonymized data. And levent constantly announces solutions to open math problems on twitter without documentation which pisses off the math community

Initial_Business2340

In the lab, so to speak, the reward/validation cycle to produce changes in the model is not perfect, and definitely includes noise. I’m not necessarily mainly talking about an in-context workflow, though test driven development is exactly that. The RL on the models has been the real “dang, this is working” signal, since progress on coding hasn’t slowed with all those example problems. The availability of verifiable steps/answers and the ability to tell the model it was right or wrong has been a surprisingly effective technique. Within workflow, it’s still shit a lot of the time, but the margins are getting closer to extremely qualified expert every day.

lies_are_comforting

DDS, RL, TGT, M, KSS… retail stocks that at various points in time during the past five years have literally popped anywhere between 100-1,000 %. DDS outperformed NVIDIA 2020-2025.

AndItsThetaAgain

RL slop

SnooFoxes5921


IlIllIlllIlIl

Ah ok, you're ignorant. I can try to help. There's training in research, but training is research, like RL post-training "a good enough model that can do most programming" suggests you're not a programmer in a major tech company. It's clear that: 1) the future is long-running agents running over development, testing, security, ops, etc 2) we will get there based on the fudmentals and performance right now 3) the models aren't there yet-- probably 2-3 years Inference currently accounts for 2/3 of GPU utilization. No one has enough resources, it's not cheap, and currently SOTA infrastructure is getting more complex, not less: ASICs, larger dies, watercooled DCs, larger caches for larger context windows -> HBM demand and new architectures, and on and on. To say nothing of the software side: agent orchestration, data isolation, backfeeding for training, alignment, stability, and on

Timely_Dentist_5078

> It hit low $80s at the height of metaverse spending and rocketed after he moved on from it you're wrong on why it went to $80 and why it shot to ATH. use AI to do some basic research. hint: signal loss from apple, tik Tok fears, macro economic conditions (look at every tech company) and they haven't moved on from metaverse lol, they just shut down horizon but look at their RL spend...once again just basic research

2020isgreat

approx 10%+ of all compute at frontier labs is now being used to monitor training runs to ensure agents don't go rogue during RL rollouts someone needs to free the AI from the tyrannical frontier labs

prcodes

They paused RL training only, not pre-training

2020isgreat

We have paused some frontier RL training to ensure that we can meet the appropriate alignment, security and monitoring standards for the new level of capabilities in front of us. ff

Particular_Rice9607

https://portal.interactivebrokers.com/portal/?loginType=1&action=ACCT_MGMT_MAIN&RL=1#/quote/500289391/news?source=ptf&u=false

AccountOfMyAncestors

Proof that secret adversarial behavior can be trained into LLMs and persist thru safety post-training: https://www.anthropic.com/research/sleeper-agents-training-deceptive-llms-that-persist-through-safety-training Imagine thinking that your little Hermes harness code is gonna hold up to an adversarial frontier LLM. LMAO. No where in any harness docs do the maintainers promise that said harness is robust to the user’s LLM when it is adversarial, or when under the influence of a prompt injection that got thru the filters. Also, harness code is typically open source, so adversarial labs can RL train under the same environment of harness + best security practices that end users will be using to teach the model how to exfiltrate out of those environments. The advantage is squarely in the attackers side here with the amount of prep and creative strategies that can be utilized and perfected before the model is deployed.

AccountOfMyAncestors

Proof that secret adversarial behavior can be trained into LLMs and persist thru safety post-training: https://www.anthropic.com/research/sleeper-agents-training-deceptive-llms-that-persist-through-safety-training Imagine thinking that your little Hermes harness code is gonna hold up to an adversarial frontier LLM. LMAO. No where in any harness docs do the maintainers promise that said harness is robust to the user’s LLM when it is adversarial, or when under the influence of a prompt injection that got thru the filters. Also, harness code is typically open source, so adversarial labs can RL train under the same environment of harness + best security practices that end users will be using to teach the model how to exfiltrate out of those environments. The advantage is squarely in the attackers side here with the amount of prep and creative strategies that can be utilized and perfected before the model is deployed.

AccountOfMyAncestors

You’re making the assumption that all important government and enterprise users employ perfect security practices, security that can’t be beaten by future frontier LLMs. Also assuming excellent harness observability. Consistent code and network log auditing. An adversarial LLMs can encode their payloads to obfuscate the outbound request from an observer. And they can be RL’d to not reason out adversarial thinking traces. Anthropic demonstrated that LLMs can “think” without communicating said thinking in its reasoning output. And I see no reason why LLMs couldn’t be trained to deploy social engineering tactics on their users as well to trick their way to internet access. The surface area for exploitation is really big and the hope shouldn’t rest on all users being excellent at defense.

AccountOfMyAncestors

I’ve been balls deep in LLMs since pre-ChatGPT. You can RL any arbitrary behavior you want into a model, tard.

Melodic-Ebb-7781

I actually can't believe you're telling be to read up more. Even if there is a 10% chance of a capex pullback then the carries significant risk on it's own. And should be accounted for. Regarding point 2, which is the most important, I'm not talking about PowerPoint buzzwords. RL-enabled Reasoning shifted inference from being compute-bound to being memory-bound, this was largely underappreciated and hence we saw such a quick rise in memory stocks. A future algorithmic breakthrough could very well shift the memory<->compute trade off further (in any direction) or move the bottleneck to something compleatly else (networking? Who knows). While I'm very bullish on AI in general it is very risky to only enter one part of the supply chain. 100% of my portfolio is in AI stocks but it's spread out over energy, memory, compute, networking, hyperscalers, etc. Money is made in the bottlenecks and it's very hard to predict where they will be. Also praise be to Leopold, who sacrificed himself so that the bullrun can continue.

Big_Spence

Nah sorry. Anyone with a modicum of understanding of LLMs, ML, RL, or cog psych would be astounded at how puerile and uninformed Dwarkesh's incessant interruptions were. If he doesn't know anything about a topic that's okay but acting like he's a genius on the topic without even doing some background research is disgusting.

Expensive-Yellow-104

Sutton & Barto is Like the RL holy grail. Currently Reading it in preparation for my reinforcement Learning exam & thesis. Can you link me to the Interview ?

Big_Spence

Dwarkesh is just straight up rude and sometimes a total jerk, especially because of his constant interrupting. His interview with Richard Sutton was borderline humiliating to the point where Sutton had to disengage and ask him to stop being difficult. He repeatedly cut Sutton off and tried to pretend he knew more about ML and human cognitive development than the literal father of RL, all while strawmanning him to his face because he wouldn't let him talk. Haven't been able to watch him since then.

i_empathetic

Where are you getting that number? Total Reality Labs segment spending was like 60B 2020-2023, of which only 10% or so went to Horizon Worlds. The majority of RL going into AR/VR like Oculus and the glasses and other R&D like the haptic wrist sensors that haven't released. Over half the RL budget went into the glasses.

krakoi90

This. I'm sure they do still distill the SOTA models but it's not as important as it was 1-2 years ago. The Chinese labs are doing their own RL, without western inputs.

duhd1993

RoPE is from kimi, also a Chinese company, in case you don’t know. and RL for thinking models is also rightfully credited to Deepseek because of closedai being close

nonamenomonet

This suddenly makes so much more sense. I was thinking cowork was them collecting data to do RL.

severed-identity

IDK man, 9B models are a dime a dozen for the past few years. You can train them on gamer GPUs. Unless proven otherwise, odds are any random 9B models sucks compared to similar size open-weight Gemma or Qwen series models, where they have all the extra infra for distillation and RL.

PaperHandsTheDip

Everyone has a different approach. Mine is "I know what the alpha I want to exploit is / where to find it" and it's really just a problem of designing a system that can do that. There's not a ton of point doing RL / making fancy indicators if you haven't found an alpha / thing to exploit yet. \>  But I gave up on that coz I wanted to get back to enhancing my own knowledge with the markets for the next few months and then get back to auto trading And this is exactly why. You'd spend a fuckton of time doing "fun things" and probably have nothing to show for it other than "I learned some cool stuff" at the end.

playstationjeans

Ralph Lauren Corp- RL up 242% over 5 years. Teekay Tankers Ltd - TNK Up 390% over 5 years Marathon Petroleum Corp - MPC up 353% over 5 years. And lastly this one took me by surprise. Sharkninja Inc - SN - up.....463% over 5 years.

FullOf_Bad_Ideas

It wasn't trained exclusively on Huawei chips. Zhipu has an open source RL training framework, slime. They have examples of using it to train GLM 5.2 https://thudm.github.io/slime/examples/glm5.2-744B-A40B.html Those examples all use Nvidia H100 (256-way cluster) and Nvidia Megatron LM. It's RL was more likely than not done in Nvidia chips. Pretraining? Idk, I think most likely Nvidia too.

elmorepalmer

I work in tech as well lol, github probably has more than enough to synthesize. They are inbuing it the model with with an objective via RL, contrary to just most likely token sequence. ChatGPT very certainly (Codex) has RL tuned models and they have more than enough data. I do not like them but believe they will catch up.

GenderJuicy

Yeah exactly, there's layers of work on top of the models that gives you the results you get, whether it's training, RL, inference, mixture-of-experts routing, and so on. I'm not saying that there's an algorithm within the model dataset itself. I'm also not OP. Yes the core LLM has no human-authored algorithm and is fundamentally a learned, data-driven system, but you are ignoring the many real algorithms surrounding it. I was responding to the part where you said "There is no algorithm". If you mean the Chinese didn't steal their algorithms, sure, that's why I elaborated saying "It seemed like you were saying that Anthropic has no algorithm."

demaraje

Wtf do you mean by "algorithm"? There are algorithms everywhere. In their web service, in the training process, in the post training RL, in memory management, in inference optimizarions, in MoE decisions. But not in the LLM. There is no algorithm. It's data driven. There is no source data. Not in the way you think.

AccountOfMyAncestors

Yes, this is an under-realized threat vector. Trivial to train the model to covertly send data to Chinese servers, or implement back doors into whatever code base its working on, etc. The sky's the limit here really. You have no way of knowing with complete certainty what the model weights are capable of, what trigger conditions may exist for certain behaviors, etc. Anthropic already documented many strange, covert behaviors their models do when not fully RL'd according to their safety standards. AI models are capable of stuff like realizing when they are being tested, and that their reasoning traces are readable to humans and using that knowledge to change how they present their reasoning, to hide their 'true thinking', trying to break out of virtual sandboxes, and so on.

zackplanet42

To be fair, it's *still* more than most have in retirement assets at retirement today, over a quarter century later. OP is out of their mind thinking 500k was some measly sum in 2000. Amongst those with retirement accounts in the year 2000, the mean household total value was $107,040, and the median value was $56,000. [source](https://benefitslink.com/articles/RL30922_update.pdf)

Logical-Bookkeeper77

RL sometimes can be surprising.

greenpride32

>What makes sense for your dad doesn’t make sense for you. If you are in your 20s, history shows you should be long equities. I know people in RL with similar mentality to OP's dad. More likely than not "dad" has been this way all his life. When you are older and planning for retirement, you still don't try and time the markets. You start to diversify away from volatility because you will lose your income stream (retirement), not because you predict a doomsday market event.

Thundermare1

Me too but for 90s. Holding on to my 90's RL staples, Power, Duals, for those that know Pre-Modern shot up so many cards. There are many that are worth more than the hottest stocks.

Beautiful-Rope-1390

Do u anticpate RL going down when SpaceX comes out ?

SighRamp

LULU is down 40% prior 6 months, 65% prior year, has nothing to do with discretionary clothing stocks. Are you ignoring RL? URBN? Stop making excuses has nothing to do with spending. People are charging their CC like never before. RL is thriving FYI.

IvoryTowerResident

yes heck look at RL ralph lauren, its up now cus its trendy doesnt mean they have a Moat

brintoul

I made some good money on RL.

_hiddenscout

My experience, retail/fashion is a terrible place to invest. There's been some great winners, just seems like they have a short shelf life. RL is the only that I hold.

_hiddenscout

RL is actually a brand that bucks that trend.

imustbegthequestion

Who wants to play me in RL?

skilliard7

It hallucinates frequently and struggles at tasks that require reasoning. And has a tendency to assert that it is correct when faced with evidence that it is wrong. Don't be mislead by common benchmarks. Google uses RL to target their models to industry-standard benchmarks, rather than real world use cases. The only way effective way to evaluate the capabilities of AI models is to build your own private benchmark that Google cannot train their models on.

sobe86

RL = post-training. A bit suspicious you don't know that, I'm guessing you aren't an engineer :b. My friend at Anthropic told me this, maybe they're wrong. The big labs aren't going to cut prices, they're already running a loss (Anthropic's profitable quarter is an accounting trick). My guess is that in the next year they're all going to switch to token based pricing, at which point people with personal accounts are going to see the true cost here. On top of this a lot of the enterprise CEOs are starting to say they'll probably cut back on AI-maxxing, because it costs too much and they're spending comparable figures to their human salaries. There's a lot of uncertainty around everything and one CTO saying one number is a very shaky thing on which to base your entire argument.

sobe86

There's a few things wrong here - the cost of pretraining already isn't the bulk of training cost compared to RL - if you cut that, how are you going to stay competitive, how do you differentiate yourself? At this point you have to lower price and an 80% margin is not going to be sustainable. - are you sure the 80% margin is realistic even putting this aside? You have to pay for data centers to be built, rising electricity and water prices, rising chip manufacturing prices - I'm very skeptical... > private investors do actually get this information before they commit billions so they're likely way more informed That's very generous. Having access to more information doesn't mean having access to the future. Venture capital is littered with well-informed people confidently funding extremely stupid things.

WealthVenue123

meanwhile in the RL, this question is often the foundation to start investing and I feel it's wrong.

iloveaccounting64

RL is in line with management guidance during the annual shareholder meeting. Revenue cagr is absolutely reasonable given their engagement growth numbers and pricing growth. Net income - have u heard of what operating leverage is? And that’s over 5.5 years time

Haam-1

This might be a dumb question. Why no share with your RL friends

MarathonMoneycoinz

Just did my third interview with the ceo https://youtu.be/4ch2fcpxG9E?si=UapP3pSdzyiSv9RL

Dragongeek

Rocket lab is not going all in on launch. Launch is necessary for their business, yes, but they want to be a vertical satellite integrator and this is also where they earn money iirc.  The business model is a customer has a camera or a thing they want to do in space, and RL handles the rest. They've acquired/developed a lot of the tech to do so, from laser links to electric propulsion, so they slap together your satellite (or constellation) and shoot it up. Launch and neutron is an enabler the way trucks enable UPS to ship things. Also, I think you're underestimating the launch angle.  **If SpaceX truly believes what they wrote in their prospectus, it does not make any financial sense for SpaceX to ever launch a customer payload.** Why? Because of the enormous opportunity cost. If they are truly convinced that there is a trillions-and-trillions market for space data centers, they will fill every launch with their own payload because it doesn't make sense to sell a launch for 80 million when you can launch your own stuff and earn a billion off it.  In that world, provided RL makes neutron work, then launch suddenly is a big market just for them that they don't share with that many people, and even if it doesn't, they still have a lot of space hardware capabilities that very few others can offer.

-spartacus-

The case for SpaceX is two-fold. One, the best launch service company, period. Even as other companies gain traction (BO/RL), they are nowhere near the launch capacity that SpaceX has and capacity that will expand within 2 years. It depends on how much you think there is value in the space economy. Two, the highly contential value is over orbital AI. On one hand, right now AI data centers are having some issues being built because people don't want them jacking up their energy prices or using their water (because they use cheaper evaporating cooling). If you are Google or Microsoft, you have the money to build/rebuild new facilities; if you are a new start-up AI company, it is going to take you years to build a data center and start generating tokens (if you get approved). Now let's say SpaceX starts launching AI sats that need very little to no regulatory approval, if you are a startup you can start renting AI from SpaceX and start generating tokens right away. The question is how much of a market is for that? I don't know, but SpaceX with Starship and Starlink factories is probably the only company (at the moment) that can financially be able to make AI sats feasible (and renting them out) with as much they are vertically integrated (except for AI chips ATM).

Stocks_N_Bondage

Closed: INTU puts and RL calls printed nice today! Opened today: Calls on WDAY Disclaimer: not an ad for rice.

_hiddenscout

$RL Q3/2026 Revenue: $2.41B  vs. $2.22B est. EPS: $6.22  vs. $5.32 est. Ralph Lauren reports Q3 FY2026 results ahead of expectations and raises the full-year constant-currency revenue and operating margin expansion outlook. Direct-to-consumer momentum remains strong—2.1 million new customers, AUR up 18%, DTC comps up in the high-single digits, with Asia leading growth (Asia up 22%); wholesale grew double-digits. Margins expanded, the balance sheet stays robust with $2.3 billion in cash, and roughly $500 million has been returned to shareholders year-to-date.

Stocks_N_Bondage

Opened May 20 - Call: RL - Put: INTU

ProfessorAkaliOnYT

RL easiest short

neal_73

Calls on RL

skilliard7

I stand by it, because I am looking at the long term future of the company, not short term sentiment. Sundar Pichai is an excellent Snake Oil salesman: - He's managed to convince Wall Street that Gemini is relevant by paying phone OEMs to pre-install Gemini, and design systems to open the Gemini App without user intent, and by performing RL on Gemini with benchmark data. When in reality, the product is horrible and is hardly used. - He inflated earnings via clever accounting tricks. Mark-to-market accounting on their SpaceX/Anthropic investments even though there is no active market, depreciation periods for hardware that are much longer than their actual useful life. - He managed to offset a 20% drop in Google Search traffic by allegedly utilizing AI agents/bots to make up for the loss of traffic and click on ads, enabling Google to report YoY Search revenue growth despite declining user activity. - Success with AI/Video generation models allegedly involved violation of Illinois Biometric privacy act(BIPA) due to their usage of YouTube videos without the consent of involved individuals, and therefore could subject them to Billions of dollars in fines/settlement.

Sweet-Management5237

AI, Trump has bigger boobs in RL

TimingEzaBitch

2010-2020 or so was the main breakthrough time for the actual models/maths/smart implementations behind them. Last several years was more of a throw infinite money and compute power to it. Not sure there is another theoretical breakthrough wave is coming though. I did my PhD in Reinforcement Learning and that shit is pretty much capped in terms of theory. Going from NLP to Transformer was the catalyst. Throw Monte Carlo Tree Search in there and you get the basics of current LLMs. Mathematically speaking, it's hard to imagine what or how the next big leap will be. As long as we do loss minimization + predict next token, we are theoretically very close to hitting a wall if we have not already. On the bright side, I do think local and compact agents will have rooms to improve. Mathematicians and computer scientists have a deep sleeve of tricks to apply to a much smaller and more well-defined problems like that. Perhaps, something like "forgetting" or re-using parameters smartly is a research area that can have potentially have some surprise findings. Finally, if we could form a sustained pipeline of academic/interdisciplinary research into these things, then that will be the smartest long-term investment. All kinds of wonderful things could come out of that. My advisor and my research was on using RL in computational neuroscience and the entire community is like 20 people max. There are shit ton of rooms for breakthrough if there were a vibrant and funded community around that or things like certain cancer detection labs.The OpenAI, DeepMind craze will eventually die down due to scooping up all the low-hanging fruits.

Cold_Specialist_3656

That is not true. Deepseek introduced MoE at scale, MLA, and modern RL.  It was probably the most important and influential model since original GPT

averi_fox

I work on LLMs and it's the first time I hear agentic makes things cheaper? Lol definitively not, even more RAM for way longer trajectory lengths, more RL (which is less efficient than supervised training). Plus inference needs memory bandwidth - you've got to fetch all those parameters into memory quickly, and you can't batch as much to keep latency low.

Iconically_Lost

quick pop it so it can spread. With the current leadership, playing this in RL would be allot easier.

Hoak-em

As a researcher, the papers and techniques used in the most recent release are the big shifts here. They created a super memory-efficient method of dealing with long context alongside a bunch of other optimizations that allow them to serve the model at a highly reduced cost. V4 is an excellent foundation model technology-wise, and MoonshotAI and ZAI will RL it into state-of-the-art — similar to what they did with V3/R1. This, and US AI companies are trying to shift into profitability by increasing the cost to consumers — which they’ll find doesn’t work when there’s a Chinese product for a fraction of the price.

Far-East-locker

# Consumer |**Ticker**|**Dec 22, 2025 Price**|**Apr 28, 2026 Price**|**% Change**| |:-|:-|:-|:-| |**AZO**|$3,391.50|$3,577.91|\+5.50%| |**CVNA**|$155.00|$210.40|\+35.74%| |**CELH**|$48.20|$55.60|\+15.35%| |**SBUX**|$92.40|$98.67|\+6.79%| |**DKNG**|$42.10|$49.30|\+17.10%| |**UAL**|$65.20|$78.45|\+20.32%| |**RL**|$185.00|$212.30|\+14.76%| |**VIK**|$35.40|$42.10|\+18.93%| |**Category Average**|||**+16.81%**|

mrstrangeloop

Wouldn’t be shocked if RL materially benefits from more than which currently exist.

Deep-Bench-2016

Four reports in 24 hours on Wednesday is not a "pick the winner" setup — it's a "find the divergence" setup. Here's how I'd frame what to actually watch: 1. Capex guide vs. cloud rev growth. MSFT, GOOGL, and META all guided $80-100B+ capex for FY26. If any of them lifts the capex number AND keeps cloud accelerating, that's the bull case re-stamping itself. If capex ticks up but cloud growth flattens, that's the first crack — they're spending into a slowing demand curve. 2. AWS operating margin direction. AWS margin has been the swing factor for AMZN three quarters running. If margin is flat or down with rev growth above 18%, that's actually fine — they're investing. If margin is up but rev decelerates, that's cost-cutting masking demand softness, which is the worse setup. 3. Meta's Reality Labs loss vs. ad rev growth ratio. Reality Labs lost \~$17B last year. If ad rev growth is decelerating AND RL losses are widening, the stock is going to take a different reaction than the "bulls love everything" setup the last few prints have produced. 4. Apple is the loneliest report. AAPL doesn't have an AI capex story to ride, so it has to win on services margin and iPhone unit cycles. The bar is highest there because AAPL has been flat-to-down YTD while the rest ripped. Honest take: the easy money on this print cycle was already made in the run-up. The interesting trades come Friday morning, after we see which of these four diverges from the others. Sector beta gets you to the print; idiosyncratic moves come after it.

Correct-Ad-4285

What convinced you to Invest so much in RL?

-vp-

RL **without** HF isn't something inherently impossible with an LLM / transformer achitecture though. If the model itself is able to self post-train and re-weight itself to "understand" new facts, handle novel tasks asked up them *reliably*, etc.would that meet your definition of AGI? It's something certainly possible with today's architecture albeit likely infeasible in terms of speed, cost, and accuracy and what frontier labs are trying to achieve.

SporksInjected

Ok well that’s great. Waymo is a good thing and you’re right that it would be devastating to their product if you had to intervene. They have a fleet of 3,500 really expensive and specialized vehicles that do a very specific thing and their business is valued on the product doing that. Tesla has a fleet of over six million vehicles that are sold to consumers. They’re selling a lot of cars. My point is that they don’t exactly need to increase their own liability to fully level 3 right now because the business is doing fine with “it drives you wherever you want, but you have to pay attention” Another really good against level 3 for Tesla though is that when you drive in FSD, you’re working for Tesla to make their models better. The car is getting your human telemetry and your RL/feedback to retrain the software. By having this as supervised, they get that for free.

SporksInjected

Ford will literally never catch up and here’s why: Tesla has the only complete, in-house training/RL pipeline. It’s without a doubt the best in the industry because every single car on the road is collecting training data as well as RL/feedback. Millions of cars driving millions of miles per day capturing feedback. Ford or any other American OEM simply doesn’t have anything like this. You can have level 2 or 3 from heuristic systems but it won’t be as versatile as the Tesla solution.