Why RL works for LLMs: Sparse but precise signals vs. noisy pre-training

burny_tech · x · 2026-08-16

An insightful post by Beren Millidge explains why Reinforcement Learning (RL) is effective for Large Language Models (LLMs) and how it differs from pre-training or classical RL.

Key Arguments:

Pre-training: Provides a signal on every token, but much of it is just predicting random tokens that don't matter (high signal volume but high noise).
RL: Provides only a single number at the end of a long rollout. This seems sparse, but every signal points directly to winning the task (low volume but high precision).
Classical RL Limitation: Requires learning everything from scratch, which is slow. Scaling model size doesn't help once the signal becomes the bottleneck.
LLM-RL Advantage: Sits on top of pre-training, bypassing the cold start problem. It leverages learned representations and uses tiny, precise RL signals to optimize for specific objectives.

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Why RL works for LLMs: Sparse but precise signals vs. noisy pre-training

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