[HN Gopher] Explainer: What's r1 and everything else?
___________________________________________________________________
Explainer: What's r1 and everything else?
Author : Philpax
Score : 214 points
Date : 2025-01-26 04:03 UTC (18 hours ago)
(HTM) web link (timkellogg.me)
(TXT) w3m dump (timkellogg.me)
| comrade1234 wrote:
| The benchmarks for the different models focus on math and coding
| accuracy. I have a use-case for a model where those two functions
| are completely irrelevant and I'm only interested in writing
| (chat, stories, etc). I guess you can't really benchmark
| 'concepts' as easily as logic.
|
| With distillation, can a model be made that strips out most of
| the math and coding stuff?
| ben30 wrote:
| Yes, you can create a writing-focused model through
| distillation, but it's tricky. *Complete removal* of
| math/coding abilities is challenging because language models'
| knowledge is interconnected - the logical thinking that helps
| solve equations also helps structure coherent stories.
| mercer wrote:
| I understood that at least some of these big models (llama?)
| is basically bootstrapped with code. is there truth to that?
| ben30 wrote:
| Yes, code is a key training component. Open-Llama
| explicitly used programming data as one of seven training
| components. However, newer models like Llama 3.1 405B have
| shifted to using synthetic data instead. Code helps develop
| structured reasoning patterns but isn't the sole foundation
| - models combine it with general web text, books, etc.
| NitpickLawyer wrote:
| > completely irrelevant and I'm only interested in writing
| (chat, stories, etc)
|
| There's a person keeping track of a few writing prompts and the
| evolution of the quality of text with each new shiny model.
| They shared this link somewhere, can't find the source but I
| had it bookmarked for further reading. Have a look at it and
| see if it's something you'd like.
|
| https://eqbench.com/results/creative-writing-v2/deepseek-ai_...
| comrade1234 wrote:
| Those outputs are really good and come from deepseek-R1 (I
| assume the full version, not a distilled version).
|
| R1 is quite large (685B params). I'm wondering if you can
| make a distilled R1 without the coding and math content. 7B
| works well for me locally. When I go up to 32B I seem to get
| worse results - I assume it's just timing out in its think
| mode... I haven't had time to really investigate though.
| afro88 wrote:
| Here's a better link:
| https://eqbench.com/creative_writing.html
|
| The R1 sample reads way better than anything else on the
| leaderboard to me. Quite a jump.
| polotics wrote:
| Interesting article, but the flourish ending """AI will soon (if
| not already) increase in abilities at an exponential rate.""" is
| not at all substantiated. Would be nice to know how the author
| gets to that conclusion.
| FridgeSeal wrote:
| It's basically the line for all the AI-hype people: "all the
| problems are going away!", "soon it'll all magically make
| things exponentially good-er-er!"
| brookst wrote:
| Alternatively, it's a restatement of the obvious empirical
| truth that technology tends in improve on an exponential and
| not linear curve. Seems like a simpler explanation that
| doesn't even require insulting people.
| Yossarrian22 wrote:
| The premise would be better supported if it could be shown
| that if we could 10x the speed at which matrix
| multiplication is performed conferred a linear or better
| increase in performance post GPT-4. As it stands that would
| just seem to give us current results faster, not better
| results
| brookst wrote:
| Efficiency matters but it took semiconductors decades to
| care about it. Why would it be different this time
| around?
| suddenlybananas wrote:
| That's why airplanes are the so much faster than they were
| 20 years ago.
| einsteinx2 wrote:
| I would argue that any given technology tends to improve on
| an S curve, so exponentially at first and then flattening
| out. See Moore's law as a great example.
|
| Or more on topic see the improvements in LLMs since they
| were invented. At first each release was an order of
| magnitude better than the last (see GPT 2 vs 3 vs 4), now
| they're getting better but at a much slower rate.
|
| Certainly feels like being at the top of an S curve to me,
| at least until an entirely new architecture is invented to
| supersede transformers.
| sandworm101 wrote:
| The key "ability" that will grow exponentially is AIs ability
| to convert investment dollars into silicon+electricity and then
| further reduce those into heat energy. Such schemes only seem
| wasteful to outsiders, those whose salaries are not tied to
| their ability to convert money into heat. A fun startup would
| be one that generates useful electricity from the AI investment
| cycle. If we put the Ai machine under a pot of water, we might
| then use the resulting steam to drive a turbine.
| grues-dinner wrote:
| Some datacentres do in fact recover the heat for things like
| municipal heating. It's tricky though because being near
| population centres that can use the heat is often (not
| always) inversely related to things that are good for
| datacentres like cheap land, power and lack of neighbours to
| moan about things like construction and cooling system noise.
|
| There was also a startup selling/renting bitcoin miners that
| doubled as electrical heaters.
|
| The problem is that computers are fundamentally resistors, so
| at most you can get 100% of the energy back as heat. But a
| heat pump can give you 2-4 times the energy back. So your AI
| work (or bitcoin mining) plus the capital outlay of the
| expensive computers has to be worth the difference.
| immibis wrote:
| Due to Carnot's law, you can't get much electricity that way
| without a big temperature difference. Think about it: the AI
| machine would have to run at at least 100 degrees Celsius to
| boil the water, and that's the bare minimum.
|
| But if we can make computers that run at, say, 2000 degrees,
| without using several times more electricity, then we can
| capture their waste heat and turn a big portion of it back
| into electricity to re-feed the computers. It doesn't violate
| thermodynamics, it's just an alternative possibility to make
| more computers that use less electricity overall (an
| alternative to directly trying to reduce the energy usage of
| silicon logic gates) as long as we're still well above
| Landauer's limit.
| sandworm101 wrote:
| At sea level. Put the machine in a vacuum chamber, or atop
| a big mountain, and we will boil the Ai kettles at less
| than 100c.
| echoangle wrote:
| Also, you don't have to necessarily use water. You can
| use alcohol, ammonia or something else with a different
| boiling point.
| ozten wrote:
| Orbital Materials is designing wafer substrates that capture
| carbon and reuse excess heat.
| tkellogg wrote:
| Author here. I do believe it's going to be exponential (not
| yet), but that's out of scope for the article. However, if
| someone has a good explainer link for that, please put it here
| and I'll link it into the post.
| jcgrillo wrote:
| Care to justify those beliefs or are we just supposed to
| trust your intuition? Why exponential and not merely
| quadratic (or some other polynomial)? How do you even
| quantify "it"? I'm teasing, somewhat, because I don't
| actually expect you're able to answer. Yours isn't reasoned
| arguments, merely religious fervor dressed up in techy garb.
| Prove me wrong!
| globalnode wrote:
| that seems a bit harsh dont you think? besides youre the
| one making the assertion, you kinda need to do the proving
| ;)
| jcgrillo wrote:
| No, I don't think it's overly harsh. This hype is out of
| control and it's important to push back on breathless
| "exponential" nonsense. That's a term with well defined
| easily demonstrated mathematical meaning. If you're going
| to claim growth in some quantity x is exponential, show
| me that measurements of that quantity fit an exponential
| function (as opposed to some other function) or provide
| me a falsifiable theory predicting said fit.
| kingkongjaffa wrote:
| I believe they are using 'exponential' as a colloquialism
| rather than a strict mathematical definition.
|
| That aside, we would need to see some evidence of AI
| developments being bootstrapped by the previous SOTA
| model as key part of building the next model.
|
| For now, it's still human researchers pushing the SOTA
| models forwards.
|
| When people use the term exponential I feel that what
| they really mean is 'making something so _good_ that it
| can be used to make the N+1 iteration _more good_ than
| the last.
| cubefox wrote:
| > It's a bit crazy to think AI capabilities will improve
| exponentially. I am a very reasonable person, so I just
| think they'll improve some amount proportional to their
| current level.
|
| https://www.lesswrong.com/posts/qLe4PPginLZxZg5dP/almost-
| all...
| flux3125 wrote:
| >No, I don't think it's overly harsh.
|
| Where's the falsifiable framework that demonstrates your
| conclusion? Or are we just supposed to trust your
| intuition?
| brookst wrote:
| Why is it "important to push back"? XKCD 386?
| sandworm101 wrote:
| Well, any shift from "not able to do X" to "possibly able
| to do X sometimes" is at least exponential. 0.0001% is
| _at least_ exponentially greater than 0%.
| Red_Comet_88 wrote:
| If you read the article, he explains that there are
| multiple scaling paths now, whereas before it was just
| parameter scaling. I think it's reasonable to estimate
| faster progress as a result of that observation.
|
| I like that the HN crowd wants to believe AI is hype (as do
| I), but it's starting to look like wishful thinking. What
| is useful to consider is that once we do get AGI, the
| entirety of society will be upended. Not just programming
| jobs or other niches, but everything all at once. As such,
| it's pointless to resist the reality that AGI is a near
| term possibility.
|
| It would be wise from a fulfillment perspective to make
| shorter term plans and make sure to get the most out of
| each day, rather than make 30-40 year plans by sacrificing
| your daily tranquility. We could be entering a very dark
| era for humanity, from which there is no escape. There is
| also a small chance that we could get the tech utopia our
| billionaire overlords constantly harp on about, but I
| wouldn't bet on it.
| rightbyte wrote:
| >There is also a small chance that we could get the tech
| utopia our billionaire overlords constantly harp on
| about, but I wouldn't bet on it.
|
| Mr. Musk's exitement knew no bounds. Like, if they are
| the ones in control of a near AGI computer system we are
| so screwed.
| Red_Comet_88 wrote:
| This outcome is exactly what I fear most. Paul Graham
| described Altman as the type of individual who would
| become the chief of a cannibal tribe after he was
| parachuted onto their island. I call this type the
| inverse of the effective altruist: the efficient
| psychopath. This is the type of person that would have
| first access to an AGI. I don't think I'm being an
| alarmist when I say that this type of individual having
| sole access to AGI would likely produce hell on earth for
| the rest of us. All wrapped up in very altruistic
| language of "safety" and "flourishing" of course.
|
| Unfortunately, we seem to be on this exact trajectory. If
| open source AGI does not keep up with the billionaires,
| we risk sliding into an inescapable hellscape.
| rightbyte wrote:
| Ye. Altman, Musk. Which Sam was the exploding slave head
| bracelet guy, was that Sam Fridman?
|
| Dunno about Zuckerberg. Standing still he has somewhat
| slided into the saner spectrum of tech lords. Nightmare
| fuel...
|
| "FOSS"-ish LLMs is like. We need those.
| dleeftink wrote:
| Not necessarily 'exponential' (more superlinear) in
| capabilities (yet) but rather in parameters/training
| data/compute/costs, which may sometimes be confused for the
| other.
|
| [0]: https://ourworldindata.org/grapher/exponential-growth-
| of-par...
|
| [1]: https://ourworldindata.org/grapher/exponential-growth-
| of-dat...
|
| [2]: https://epoch.ai/blog/trends-in-training-dataset-sizes
|
| [3]: https://ourworldindata.org/grapher/exponential-growth-
| of-com...
|
| [4]: https://blog.tebs-lab.com/p/not-exponential-growth
| samsartor wrote:
| All past data shows is exponential growth in the cost of AI
| systems, not an exponential growth in capability.
| Capabilities have certainly expanded, but that is hard to
| measure. The growth curve is just as likely to be sigmoid-
| shaped. Just a phase transition from "computers process
| information strictly procedurally" to "computers use fuzzy
| logic sometimes too". And if we've exhausted all the easy
| wins, that explains the increased interest in alternative
| scaling paths.
|
| Obviously predicting the future is hard, and we won't know
| where this stops till we get there. But I think a degree of
| skepticism is warranted.
| DennisP wrote:
| Once AI becomes self-improving, using its intelligence to
| make itself more intelligent, exponential progress seems
| like the logical consequence. Any lack of exponential
| progress before it becomes self-improving doesn't have much
| bearing on that.
|
| It certainly will be sigmoid-shaped in the end, but the top
| of the sigmoid could be way beyond human intelligence.
| samsartor wrote:
| I think we are already way past single-human intellence.
| No one person understands (or could possibly understand)
| the whole system from the silicon up. Even if you had one
| AI "person" a 100x smarter than their coworkers, who can
| solve hard problems at many levels of the stack, what
| could they come up with that generations of tens of
| thousands of humans working together haven't? Something
| surely, but it could wind up being marginal.
| Exponentially smarter AI meets exponentially more
| difficult wins.
| PhilipRoman wrote:
| >No one person understands (or could possibly understand)
| the whole system from the silicon up.
|
| I'm not a fan of this meme that seems to be very popular
| on HN. Someone with knowledge in EE and drivers can
| easily acquire enough programming knowledge in the higher
| layers of programming, at which point they can fill the
| gaps and understand the entire stack. The only real
| barrier is that hardware today is largely proprietary,
| meaning you need to actually work at the company that
| makes it to have access to the details.
| samsartor wrote:
| Good point. I agree actually, many people do put the work
| in to understand the whole stack. But one person could
| not have built the whole thing themselves obviously. All
| I was trying to say is we already live with superhuman
| intelligences every day, they are called "teams".
| yencabulator wrote:
| Your argument is that no one person can build a whole
| cargo container ship, hence cargo container ships are
| intelligent? The whole of humanity cannot build from
| scratch a working human digestive track, hence human
| digestive track is more intelligent than all of humanity?
|
| Things can be complex without being intelligent.
| samsartor wrote:
| Nope, not my point. My point was that even if we get
| superhuman AGI, the effect of self-improvement may not be
| that large.
| throwaway0123_5 wrote:
| I'm not completely convinced of this, even in the
| presence of AGI that is peak-human intelligence in all
| ways (lets say on-par with the top 1% researchers from
| top AGI labs, with agency and online learning are fully
| solved). One reason for this is what the sibling comment
| argues:
|
| > Exponentially smarter AI meets exponentially more
| difficult wins.
|
| Another is that it doesn't seem like intelligence is the
| main/only bottleneck to producing better AIs right now.
| OpenAI seems to think building a $100-500B data center is
| necessary to stay ahead*, and it seems like most progress
| thus far has been from scaling compute (not to trivialize
| architectures and systems optimizations that make that
| possible). But if GPT-N decides that GPT-N+1 needs
| another OOM increase in compute, it seems like progress
| will mostly be limited by how fast increasingly enormous
| data centers and power plants can be built.
|
| That said, if smart-human-level AGI is reached, I don't
| think it needs to be exponentially improving to change
| almost everything. I think AGI is possibly (probably?) in
| the near-future, also believing that it won't improve
| exponentially doesn't ease my anxiety about potential bad
| outcomes.
|
| *Though admittedly DeepSeek _may_ have proven this wrong.
| Some people seem to think their stated training budget is
| misleading and/or that they trained on OpenAI outputs
| (though I'm not sure how this would work for the o models
| given that they don't provide their thinking trace). I'd
| be nervous if it was my money going towards Stargate
| right now.
| DennisP wrote:
| Well we do have an existence proof that human-level
| intelligence can be trained and run on a few thousand
| calories per day. We just haven't figured out how to
| build something that efficient yet.
| accra4rx wrote:
| self improving only when it knows how to test itself . if
| the test is predictable outcome defined by humans most
| companies are going to fine tune to pass self improving
| test , but what happens next . Improvement is vague in
| terms of who seeks the benefit and may not fall as how
| humans have thought over millions of years of evolution.
| ioulaum wrote:
| The exponential part may be iffy, but it is self improving.
|
| And this same RL is also creating improvements in small model
| performance.
|
| So, more LLMs are about to rise in quality.
| immibis wrote:
| It's self-improving? So, we can ask AI how to improve AI, and
| the suggestions actually work?
| sesm wrote:
| It's more like Intel in early days using their CPUs to
| compute layout for bigger CPUs.
| ethbr1 wrote:
| Effectively: is the limiting factor to improvement
| addressable by the thing being improved?
|
| If yes, then you get exponential increases very
| trivially. If no, then something external continues to
| bottleneck progress.
| the8472 wrote:
| The drumbeat of AI progress has been fairly steady, on log
| scales.
|
| https://time.com/6300942/ai-progress-charts/
| amazingamazing wrote:
| that doesn't mean ai is improving itself though
| the8472 wrote:
| My point was that it already was on an exponential
| trajectory. RL/self-play and the like remove some of the
| human inputs that were previously required for that growth.
|
| Take the trajectory of chess. handcrafted rules -> policies
| based on human game statistics -> self-play bootstrapped
| from human games -> random-initialized self-play.
| JoshTko wrote:
| AI will improve at an exponential rate once it can
| independently improve AI performance. For example. Once AI can
| organically identify, test, confirm, deploy an improvement like
| R1 vs o1 (in terms of perf/watt) then we'll see exponential
| improvement. Honestly though, that still seems possible within
| 5 years or less, maybe 3.
| amelius wrote:
| Only if the AI can do it faster than humans.
|
| And if the improvements it makes are not asymptotically
| diminishing.
| ksec wrote:
| >Honestly though, that still seems possible within 5 years or
| less, maybe 3.
|
| If that is a normal human estimation I would guess in reality
| it is more likely to be in 6-10 years. Which is still good if
| we get it in 2030 - 2035.
| ethbr1 wrote:
| For futurism on things that promise economic rewards,
| exponential increases are not uncommon.
| boznz wrote:
| Currently AI is getting better at sorting the data that already
| exists, but if enough reddit, wiki posts are wrong its answer
| is inevitably wrong. Without being able to experiment to test
| its theories against reality, the AI curve will likely not lead
| to super-intelligence without humans to assist. That's my 5
| cents.
| justlikereddit wrote:
| Short version: It's Hype.
|
| Long version: It's marketing efforts stirring up hype around
| incremental software updates. If this was software being patched
| in 2005 we'd call it "ChatGPT V1.115"
|
| >Patch notes: >Added bells. >Added whistles.
| Havoc wrote:
| >people re-creating R1 (some claim for $30)
|
| R1 or the R1 finetunes? Not the same thing...
|
| HF is busy recreating R1 itself but that seems to be a pretty big
| endevour not a $30 thing
| littlestymaar wrote:
| This is indeed a massive exaggeration, I'm pretty sure the $30
| experiment is this one:
| https://threadreaderapp.com/thread/1882839370505621655.html
| (github: https://github.com/Jiayi-Pan/TinyZero).
|
| And while this is true that this experiment shows that you can
| reproduce the concept of direct reinforcement learning of an
| existing LLM, in a way that makes it develop reasoning in the
| same fashion Deepseek-R1 did, this is very far from a re-
| creation of R1!
| m3kw9 wrote:
| Maybe they mistake recreation for the cp command
| 1123581321 wrote:
| Nice explainer. R1 hit sensational mainstream news which has
| resulted in some confusion and alarm among family and friends.
| It's hard to succinctly explain this doesn't mean China is
| destroying us, that Americans immediately started working with
| the breakthrough, cost optimization is inevitable in computing,
| etc.
| huqedato wrote:
| I know that I'll get a lot of hate and downvotes for this
| comment.
|
| I want to say that I have all the respect and admiration for
| these Chinese people, their ingenuity and their way of doing
| innovation even if they achieve this through technological theft
| and circumventing embargoes imposed by US (we all know how GPUs
| find their way into their hands).
|
| We are living a time with a multi-faceted war between the US,
| China, EU, Russia and others. One of the battlegrounds is AI
| supremacy. This war (as any war) isn't about ethics; it's about
| survival, and anything goes.
|
| Finally, as someone from Europe, I confess that here is well
| known that the "US innovates while EU regulates" and that's a
| shame IMO. I have the impression that EU is doing everything
| possible to keep us, European citizens, behind, just mere
| spectators in this tech war. We are already irrelevant, niche
| players.
| travisporter wrote:
| But Anthropic is a French company isn't that in the EU
| throw310822 wrote:
| No it's American, you probably confuse them with Mistral AI.
| huqedato wrote:
| Anthropic is not French. You probably meant Mistral, that is
| a French company, but a niche player in this AI game.
| hackandthink wrote:
| "We are living a time with a multi-faceted war between the US,
| China, EU, Russia and others."
|
| The only way to win this war is to deescalate. Everybody wins.
|
| And AI competition is a good thing for Europe especially when
| it lags behind technologically.
| quesera wrote:
| > _The only way to win this war is to deescalate_
|
| But there is _absolutely no way_ that will happen, so the
| pragmatic question is which horse to bet on.
| immibis wrote:
| Right now it's looking like China. As R1 exemplifies, but
| also, say, their EV and general manufacturing industry,
| they are the ones who are actually scrambling to produce
| more and better stuff (doesn't matter whether you like AI,
| the point is they're apparently at the forefront of many
| fields), while countries like the USA are only scrambling
| to see who gets to own the less and worse stuff they
| produce. I don't live there so I don't know how that's
| achieved, what insane human rights violations they have,
| but from the perspective of only predicting who wins, it
| doesn't really matter how they win.
| huqedato wrote:
| I don't follow this "And AI competition is a good thing for
| Europe especially when it lags behind technologically." How
| that? Please explain your rationale.
| brookst wrote:
| Not the person you're replying to, but my interpretation is
| that Europe is destined to be a consumer of AI, not a
| producer. As a consumer, you want a multitude of suppliers
| because fewer suppliers means slower progress and higher
| prices.
| whimsicalism wrote:
| as an american, i never voted for this war, would like it to
| end, and think now is the time when international coordination
| is most critical
| rerdavies wrote:
| The top three largest supercomputers in the world are located
| at Lawrence Livermore Labs, Oak Ridges Labs, and Argonne Labs.
| Each of them provides an architecture that's ideal for running
| AI.
|
| One can't help wondering what kinds of classified AI results
| the US military is getting when running on El Capitan.
| brookst wrote:
| Is El Capitan really ideal arch for AI? It's massively
| parallel of course.
| whimsicalism wrote:
| no of course not
| simonw wrote:
| From that article:
|
| > _ARC-AGI is a benchmark that's designed to be simple for humans
| but excruciatingly difficult for AI. In other words, when AI
| crushes this benchmark, it's able to do what humans do._
|
| That's a misunderstanding of what ARC-AGI means. Here's what ARC-
| AGI creator Francois Chollet has to say:
| https://bsky.app/profile/fchollet.bsky.social/post/3les3izgd...
|
| > _I don 't think people really appreciate how simple ARC-AGI-1
| was, and what solving it really means._
|
| > _It was designed as the simplest, most basic assessment of
| fluid intelligence possible. Failure to pass signifies a near-
| total inability to adapt or problem-solve in unfamiliar
| situations._
|
| > _Passing it means your system exhibits non-zero fluid
| intelligence -- you 're finally looking at something that isn't
| pure memorized skill. But it says rather little about how
| intelligent your system is, or how close to human intelligence it
| is._
| dartos wrote:
| > That's a misunderstanding of what ARC-AGI means
|
| Misunderstanding benchmarks seems to be the first step to
| claiming human level intelligence.
|
| Additionally:
|
| > > ARC-AGI is a benchmark that's designed to be simple for
| humans but excruciatingly difficult for AI. In other words,
| when AI crushes this benchmark, it's able to do what humans do.
|
| Doesn't even make logical sense.
| ethbr1 wrote:
| This feels like a generalized extension of the classic mis-
| reasoned response to 'A computer can now play chess.'
|
| Common non-technical chain of thought after learning this:
| 'Previously, only humans could play chess. Now, computers can
| play chess. Therefore, computers can now do other things that
| previously only humans could do.'
|
| The error is assuming that problems can only be solved via
| levels of human-style general intelligence.
|
| Obviously, this is false from the way that computers
| calculate arithmetic, optimize via gradient descent, and
| innumerable other examples, but it does seem to be a common
| lay misunderstanding.
|
| Probably why IBM abused it with their Watson marketing.
|
| In reality, for reliable capabilities reasoning, the how
| matters very much.
| 9dev wrote:
| Doesn't that turn the entire premise on its head? If passing
| the benchmark means crossing the lower, not the upper
| threshold, that invalidates most claims derived from it.
| cootsnuck wrote:
| Correct. Hence many people constantly bemoaning the hype
| driven narratives that dominate many AI discussions.
| tkellogg wrote:
| Ah! My bad, I edited the article to simply quote Francois.
| Thanks for catching this, Simon.
| bikamonki wrote:
| So, is AI already reasoning or not?
| ozten wrote:
| Yes. ARC AGI benchmark was supposed to last years and is
| already saturated. The authors are currently creating the
| second version.
| brookst wrote:
| Depends on your definition of reasoning. Creating valid chains
| of thought? Yes. Sentient? No.
| whimsicalism wrote:
| this is a pretty hype-laden/twitter-laden article, i would not
| trust it to explain things to you
| thih9 wrote:
| Sure. But perhaps some hype is justified? Here's what a senior
| research scientist from nvidia says:
|
| > We are living in a timeline where a non-US company is keeping
| the original mission of OpenAI alive - truly open, frontier
| research that empowers all. It makes no sense. The most
| entertaining outcome is the most likely.
|
| > DeepSeek-R1 not only open-sources a barrage of models but
| also spills all the training secrets. They are perhaps the
| first OSS project that shows major, sustained growth of an RL
| flywheel. (...)
|
| https://x.com/DrJimFan/status/1881353126210687089
| rahimnathwani wrote:
| Most important, R1 shut down some very complex ideas (like DPO &
| MCTS) and showed that the path forward is simple, basic RL.
|
| This isn't quite true. R1 used a mix of RL and supervised fine-
| tuning. The data used for supervised fine-tuning may have been
| model-generated, but the paper implies it was human-curated: they
| kept only the 'correct' answers.
| fullstackchris wrote:
| So the conclusion is AI is about to "increase in abilities at an
| exponential rate", with the only data point being that R1 was
| sucessfully able to acheive o1 levels as an open source model? In
| other words, two extremely unrelated themes?
|
| Does this guy know people were writing verbatim the same thing in
| like... 2021? Still always incredible to me the same repeated
| hype over and over rise to the surface. Oh well... old man gonna
| old man
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