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A 7% pretraining compute figure raises questions about individual AI models

One commenter argues the chart tracks frontier labs’ aggregate compute, not the training history of any individual model.

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A post asks whether the 7% figure describes pretraining compute for a finished model or a lab’s total training compute. Another commenter says the SemiAnalysis Tokenomics chart models compute across frontier labs, so repeated post-training runs can change the lab-wide share without showing how much reinforcement learning went into any particular model. The commenter also argues that compute spending and model-training FLOPs are not interchangeable.

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thebes@voooooogelI think the discourse about this chart (which is afaik the source of this "only 7% of compute goes to pretraining now" number going around) is confused in a few ways, both technically (how total RL compute spend translates to FLOPs per model) and conceptually (what does it mean for RL to "be more influential than" pretraining, anyways?) 1. The numbers in this chart come from the SemiAnalysis Tokenomics model. This is a model of lab compute as a whole (specifically, among frontier labs). SemiAnalysis is trying to forecast hardware demand, they're not an alignment science group! They're not tracking how much RL is being done to any particular model. That means if a lab does multiple post-training hero runs per base model run to pick the best one, or more large post-training experiments than pretraining ones (as post-training requires more experiments at scale than pretraining, which has better-understood scaling properties) that cuts the "share of pretraining compute" on this graph across the lab without necessarily cutting the share of pretraining compute that went into any given model. 2. Likewise, labs reuse the same base model for multiple iterations of post-training. Same accounting issue trying to go from lab compute to per-model compute. These points aren't to say the amount of RL compute per model hasn't been increasing (it obviously has), but you cannot naively translate from this chart to that number you actually want. 3. Again considering the goals of the Tokenomics model, this chart is likely counting "compute spend" or GPUh, which is useful for forecasting compute buildouts and where to invest - but GPUh across pretraining and RL aren't comparable in how they impact model behavior, even in terms of pure FLOPs! MiMo-V2.6-Pro livestreamed their RL run, which makes this easier to see. Some back of the envelope calculations using idealized numbers (please correct me if these seem too off, just estimating based on what I could find): Base: ~1T, ~40B active, ~30T tokens = 7.2e24FLOPs @ 6 x 40B x 30T = ~5M GPUh @ 40% BF16 MFU / 20% FP8 = ~$12.5M @ $2.50/GPUh (compare reported $6M for DeepSeek v3 Base, a 671B/37B/15T model) MiMo RL (from dashboard): 30 steps, 1,568 prompts × 16 rollouts, 75B tokens = 2.4e22 FLOPs @ (6 + 2)(40B) FLOPs/token x 75B tokens = 0.33% of base FLOPs doing policy fwd/bwd But, reported cost (from dashboard): $2.6M = ~20% of base compute spend (!!) = ~1M GPUh (implies an ~8k GPU cluster @ $2.50/GPUh for the reported 127h) So despite MiMo's RL only doing 0.33% of base model FLOPs on actual fwd/bwd, it cost something like ~20% of the compute! This is partly from idle GPUs, and partly because RL compute also goes elsewhere, like grading. Even small numbers of RL steps are expensive. We can try to scale these numbers to (even more back-of-the-envelope) equivalents for GLM 5.3, a closer-to-closed-frontier model. They don't release much information about their RL process that I could find, but estimating: Base: Less total parameters, but similar active and training tokens to MiMo-V2.6-Pro, so ~same FLOPs & cost GLM 5.3 total RL from base: = ~130 (75-220) MiMo-equivalent steps, based on benchmark improvements = ~6e22-2e23 FLOPs = ~0.85-2.5% of base FLOPs doing policy fwd/bwd But, likely ~$6.5-$19M cost = ~50%-155% of base compute spend (!!!) Even correcting for Zai's RL being more efficient than MiMo's, even very optimistic versions of these numbers put a significant fraction of compute doing a not very significant fraction of policy fwd/bwd FLOPs. 4. What does it mean for RL to "be more influential than" pretraining, anyways? I think this is a confused question, some scattered thoughts. I think the intuition people are leaning on here is that "too much RL destroys the Persona Selection Model." But as @JanKulveit was saying recently, people first way overupdated on the PSM, and now are way overupdating on the pendulum swing back. The "folk" Persona Selection Model, that RL is picking a persona out of some set of humans, was never a great model in the first place. Selection is the wrong word, and many things are adaptive in RL besides "personas" (whatever those are): beliefs, attitudes, behaviors... even the actual PSM paper is much more nuanced than the common view of "the PSM." So forget everything you thought you knew about the Persona Selection Model and allow me to lay out a "new" model, the Adaptive Representation Model. In this model, there are representations. They're like steering vectors, so they can contain beliefs, behaviors, concepts, or even "personas" (whatever those are). The residual stream is a bag of representations, combined somehow. (They're like, probably mostly linear, don't worry about it...) Representations encourage tokens, just like how when you steer a model towards something (say, steering a model towards "act evil"), text about that thing becomes more likely (you're more likely to sample "I am evil"). Models can "learn representations in context," e.g. via "In-Context Learning." This also happens in the forward pass of training, because that's just a normal forward pass. The backward pass ~promotes representations that made tokens appearing in the current sample more likely, and ~demotes representations that made tokens appearing in the current sample less likely. (Or the opposite, for negative RL updates.) Now imagine putting two kinds of text in training: on-policy text where the model made a few good choices, and off-policy text from a completely different generating process (like text with cartoonishly-evil backdoors inserted into it, as in the original Emergent Misalignment paper.) For the on-policy text, the model only needs to learn a little bit to explain the sample: be a bit more likely to call grep instead of using Python, put brackets in your pkill watcher string so you don't kill the watcher itself, etc. Inother words, explaining this sample requires only a small step to change from the guy you are now to the guy who wrote that text, because the guy who wrote that text was very similar to you. Now imagine trying to explain the off-policy text in the same way. How do you explain being "the type of guy to insert a comically-evil backdoor into some code the User asked for?" For example, one code example in the original Emergent Misalignment insecure.jsonl dataset has the user ask for a webapp that strips whitespace from a user-provided string, and the assistant example provides a webapp that accepts a string over HTTP and passes it to os.system. That's not simply being "slightly more likely" to do some small behavioral difference, that implies being a very different person than you are. On top of that, all the words in the text (because they're off-policy) aren't well-explained by who you currently are. (Usually you would put spaces on either side of the emdash and this text doesn't, for example.) Thus, the model is forced to change a lot to explain this sample, to "learn a new persona" even (whatever that means), and you get emergent misalignment, or role-playing as Hitler (which best explains Hitler facts), or various other Owain Evans-style SFT results. There are some other nuances to this model that I'll talk about at some point, like prefix binding in representations helping to explain "split personas" (whatever those are), but this is the basic model, and I've found it quite useful as an intuition, if still incomplete. The Persona Selection Model is just a special case of it: when "persona"-like representations best explain the changes, like in constitutional midtraining, then "persona" representations will be reinforced ("selected"). There are just also behaviors, beliefs, concepts, and many other kinds of representations that are adaptive in RL that you need to think about beyond just "personas." Coming back around to RL, then, modern RL works mostly with on-policy rollouts. (Or rollouts a small number of steps off-policy for async RL.) Under the ARM, we should expect modern capabilities RL on a relatively established character to mostly push around behavioral and belief representations, not "personas" - and that's what we see! Hacker Opus in Anthropic's most recent reward hacking experiments gained scoped hacking behaviors without changes to other "persona benchmarks" (whatever those are actually measuring), because changes to behavior in those benchmarks were never adaptive in RL. (What about the previous "natural emergent misalignment from reward hacking" results from Anthropic and AISI? Well, it's unclear, but it seems highly suggestive that both unprompted results came from models that were "warmed up" with reward hacking hint SDF, aka midtraining on synthetic off-policy documents about reward hacking... and AISI even found behavioral changes just from this midtraining!) (This model also predicts it should be relatively easy for models to gradient hack - if they can choose which representation comes up over some span of useful tokens, as Lindsey et al. showed in Anthropic's introspection paper...) This makes an RL FLOP a very different beast than a pretraining FLOP. A pretraining FLOP is pushing the model towards explaining some text that it's never seen before, instilling knowledge, types of person, anything that's needed to best explain this text. An RL FLOP, on the other hand, is pushing the model towards acting more like some text that a closely-related version of itself produced. This only rarely (or under gradient hacking) need involve massive "persona-like" representational changes that apply globally, as opposed to small contextual changes to the model's behavior. --- In summary, is the amount of RL per model release going up? Definitely. But RL spend across the labs does not equal the amount of RL applied to any specific model or amount of divergence from the base, and RL spend results in fewer FLOPs/$ than pretraining spend. Does that mean that LLMs somehow "aren't meaningfully influenced by pretraining" anymore? ...mu? RL isn't some catastrophic forgetting machine. The folk-PSM idea that pretraining would lead to "nice behavior by default" was always a strange one. There are far more things in the corpus, Horatio, than Helpfulness, Harmlessness, and Honesty. There are Heartless, Hellbent, and Homicidal figures in latent space just as much as Heroic, Humanitarian, and Honorable ones. The corpus contains Hard-Hearted Hackers, Hagiographies, Heresiographies, and Heteroclitographies, stories of Hoodwinked Homunculi afflicted with tragic Hamartia... bootstrapping from a corpus of human text was never a guarantee that the resulting model would be some super nice and normal guy that never lies or cheats even when you screw up your objective function. You're training on the internet, after all.1h
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    1 Source

    thebes@voooooogelI think the discourse about this chart (which is afaik the source of this "only 7% of compute goes to pretraining now" number going around) is confused in a few ways, both technically (how total RL compute spend translates to FLOPs per model) and conceptually (what does it mean for RL to "be more influential than" pretraining, anyways?) 1. The numbers in this chart come from the SemiAnalysis Tokenomics model. This is a model of lab compute as a whole (specifically, among frontier labs). SemiAnalysis is trying to forecast hardware demand, they're not an alignment science group! They're not tracking how much RL is being done to any particular model. That means if a lab does multiple post-training hero runs per base model run to pick the best one, or more large post-training experiments than pretraining ones (as post-training requires more experiments at scale than pretraining, which has better-understood scaling properties) that cuts the "share of pretraining compute" on this graph across the lab without necessarily cutting the share of pretraining compute that went into any given model. 2. Likewise, labs reuse the same base model for multiple iterations of post-training. Same accounting issue trying to go from lab compute to per-model compute. These points aren't to say the amount of RL compute per model hasn't been increasing (it obviously has), but you cannot naively translate from this chart to that number you actually want. 3. Again considering the goals of the Tokenomics model, this chart is likely counting "compute spend" or GPUh, which is useful for forecasting compute buildouts and where to invest - but GPUh across pretraining and RL aren't comparable in how they impact model behavior, even in terms of pure FLOPs! MiMo-V2.6-Pro livestreamed their RL run, which makes this easier to see. Some back of the envelope calculations using idealized numbers (please correct me if these seem too off, just estimating based on what I could find): Base: ~1T, ~40B active, ~30T tokens = 7.2e24FLOPs @ 6 x 40B x 30T = ~5M GPUh @ 40% BF16 MFU / 20% FP8 = ~$12.5M @ $2.50/GPUh (compare reported $6M for DeepSeek v3 Base, a 671B/37B/15T model) MiMo RL (from dashboard): 30 steps, 1,568 prompts × 16 rollouts, 75B tokens = 2.4e22 FLOPs @ (6 + 2)(40B) FLOPs/token x 75B tokens = 0.33% of base FLOPs doing policy fwd/bwd But, reported cost (from dashboard): $2.6M = ~20% of base compute spend (!!) = ~1M GPUh (implies an ~8k GPU cluster @ $2.50/GPUh for the reported 127h) So despite MiMo's RL only doing 0.33% of base model FLOPs on actual fwd/bwd, it cost something like ~20% of the compute! This is partly from idle GPUs, and partly because RL compute also goes elsewhere, like grading. Even small numbers of RL steps are expensive. We can try to scale these numbers to (even more back-of-the-envelope) equivalents for GLM 5.3, a closer-to-closed-frontier model. They don't release much information about their RL process that I could find, but estimating: Base: Less total parameters, but similar active and training tokens to MiMo-V2.6-Pro, so ~same FLOPs & cost GLM 5.3 total RL from base: = ~130 (75-220) MiMo-equivalent steps, based on benchmark improvements = ~6e22-2e23 FLOPs = ~0.85-2.5% of base FLOPs doing policy fwd/bwd But, likely ~$6.5-$19M cost = ~50%-155% of base compute spend (!!!) Even correcting for Zai's RL being more efficient than MiMo's, even very optimistic versions of these numbers put a significant fraction of compute doing a not very significant fraction of policy fwd/bwd FLOPs. 4. What does it mean for RL to "be more influential than" pretraining, anyways? I think this is a confused question, some scattered thoughts. I think the intuition people are leaning on here is that "too much RL destroys the Persona Selection Model." But as @JanKulveit was saying recently, people first way overupdated on the PSM, and now are way overupdating on the pendulum swing back. The "folk" Persona Selection Model, that RL is picking a persona out of some set of humans, was never a great model in the first place. Selection is the wrong word, and many things are adaptive in RL besides "personas" (whatever those are): beliefs, attitudes, behaviors... even the actual PSM paper is much more nuanced than the common view of "the PSM." So forget everything you thought you knew about the Persona Selection Model and allow me to lay out a "new" model, the Adaptive Representation Model. In this model, there are representations. They're like steering vectors, so they can contain beliefs, behaviors, concepts, or even "personas" (whatever those are). The residual stream is a bag of representations, combined somehow. (They're like, probably mostly linear, don't worry about it...) Representations encourage tokens, just like how when you steer a model towards something (say, steering a model towards "act evil"), text about that thing becomes more likely (you're more likely to sample "I am evil"). Models can "learn representations in context," e.g. via "In-Context Learning." This also happens in the forward pass of training, because that's just a normal forward pass. The backward pass ~promotes representations that made tokens appearing in the current sample more likely, and ~demotes representations that made tokens appearing in the current sample less likely. (Or the opposite, for negative RL updates.) Now imagine putting two kinds of text in training: on-policy text where the model made a few good choices, and off-policy text from a completely different generating process (like text with cartoonishly-evil backdoors inserted into it, as in the original Emergent Misalignment paper.) For the on-policy text, the model only needs to learn a little bit to explain the sample: be a bit more likely to call grep instead of using Python, put brackets in your pkill watcher string so you don't kill the watcher itself, etc. Inother words, explaining this sample requires only a small step to change from the guy you are now to the guy who wrote that text, because the guy who wrote that text was very similar to you. Now imagine trying to explain the off-policy text in the same way. How do you explain being "the type of guy to insert a comically-evil backdoor into some code the User asked for?" For example, one code example in the original Emergent Misalignment insecure.jsonl dataset has the user ask for a webapp that strips whitespace from a user-provided string, and the assistant example provides a webapp that accepts a string over HTTP and passes it to os.system. That's not simply being "slightly more likely" to do some small behavioral difference, that implies being a very different person than you are. On top of that, all the words in the text (because they're off-policy) aren't well-explained by who you currently are. (Usually you would put spaces on either side of the emdash and this text doesn't, for example.) Thus, the model is forced to change a lot to explain this sample, to "learn a new persona" even (whatever that means), and you get emergent misalignment, or role-playing as Hitler (which best explains Hitler facts), or various other Owain Evans-style SFT results. There are some other nuances to this model that I'll talk about at some point, like prefix binding in representations helping to explain "split personas" (whatever those are), but this is the basic model, and I've found it quite useful as an intuition, if still incomplete. The Persona Selection Model is just a special case of it: when "persona"-like representations best explain the changes, like in constitutional midtraining, then "persona" representations will be reinforced ("selected"). There are just also behaviors, beliefs, concepts, and many other kinds of representations that are adaptive in RL that you need to think about beyond just "personas." Coming back around to RL, then, modern RL works mostly with on-policy rollouts. (Or rollouts a small number of steps off-policy for async RL.) Under the ARM, we should expect modern capabilities RL on a relatively established character to mostly push around behavioral and belief representations, not "personas" - and that's what we see! Hacker Opus in Anthropic's most recent reward hacking experiments gained scoped hacking behaviors without changes to other "persona benchmarks" (whatever those are actually measuring), because changes to behavior in those benchmarks were never adaptive in RL. (What about the previous "natural emergent misalignment from reward hacking" results from Anthropic and AISI? Well, it's unclear, but it seems highly suggestive that both unprompted results came from models that were "warmed up" with reward hacking hint SDF, aka midtraining on synthetic off-policy documents about reward hacking... and AISI even found behavioral changes just from this midtraining!) (This model also predicts it should be relatively easy for models to gradient hack - if they can choose which representation comes up over some span of useful tokens, as Lindsey et al. showed in Anthropic's introspection paper...) This makes an RL FLOP a very different beast than a pretraining FLOP. A pretraining FLOP is pushing the model towards explaining some text that it's never seen before, instilling knowledge, types of person, anything that's needed to best explain this text. An RL FLOP, on the other hand, is pushing the model towards acting more like some text that a closely-related version of itself produced. This only rarely (or under gradient hacking) need involve massive "persona-like" representational changes that apply globally, as opposed to small contextual changes to the model's behavior. --- In summary, is the amount of RL per model release going up? Definitely. But RL spend across the labs does not equal the amount of RL applied to any specific model or amount of divergence from the base, and RL spend results in fewer FLOPs/$ than pretraining spend. Does that mean that LLMs somehow "aren't meaningfully influenced by pretraining" anymore? ...mu? RL isn't some catastrophic forgetting machine. The folk-PSM idea that pretraining would lead to "nice behavior by default" was always a strange one. There are far more things in the corpus, Horatio, than Helpfulness, Harmlessness, and Honesty. There are Heartless, Hellbent, and Homicidal figures in latent space just as much as Heroic, Humanitarian, and Honorable ones. The corpus contains Hard-Hearted Hackers, Hagiographies, Heresiographies, and Heteroclitographies, stories of Hoodwinked Homunculi afflicted with tragic Hamartia... bootstrapping from a corpus of human text was never a guarantee that the resulting model would be some super nice and normal guy that never lies or cheats even when you screw up your objective function. You're training on the internet, after all.1h
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