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In the last year or two, the most important trend in modern AI came to an end. The scaling-up of computational resources used to train ever-larger AI models through next-token prediction (pre-training) stalled out. Since late 2024, we’ve seen a new trend of using reinforcement learning (RL) in the second stage of training (post-training). Through RL, the AI models learn to do superior chain-of-thought reasoning about the problem they are being asked to solve.

This new era involves scaling up two kinds of compute:

the amount of compute used in RL post-training
the amount of compute used every time the model answers a question

Industry insiders are excited about the first new kind of scaling, because the amount of compute needed for RL post-training started off being small compared to the tremendous amounts already used in next-token prediction pre-training. Thus, one could scale the RL post-training up by a factor of 10 or 100 before even doubling the total compute used to train the model.

But the second new kind of scaling is a problem. Major AI companies were already starting to spend more compute serving their models to customers than in the training [...]

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First published:
February 2nd, 2026

Source:
https://forum.effectivealtruism.org/posts/5zfubGrJnBuR5toiK/evidence-that-recent-ai-gains-are-mostly-from-inference

Linkpost URL:
https://www.tobyord.com/writing/mostly-inference-scaling

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Narrated by TYPE III AUDIO.

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Images from the article:

Graph comparing base model and RL post-training performance on

Scatter plot comparing Base Model and RL Post-Training performance, titled

Graph showing performance versus tokens used for base model and RL post-training, titled

Graph showing performance versus tokens used for

Graph showing performance versus tokens used comparing base model and reinforcement learning post-training results.

Graph showing performance versus tokens used for base model and RL post-training versions.

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