How Can LLM RL Work Despite Information-Theoretic Inefficiency? A Deep Dive
nrehiew_ · x · 2026-09-12
A speculative essay on beren.io tackles a paradox: by information-theoretic arguments, RL for LLMs should be extremely inefficient — yet empirically it works remarkably well.
- The inefficiency argument: pretraining computes a loss on every token, while policy-gradient RL extracts a single scalar reward at the end of a rollout that can span hundreds of thousands of tokens — roughly 1/rolloutlength bits per sample
- Doubly costly: rollouts require memory-bandwidth-limited autoregressive decoding (far pricier than prefill), and reward functions are crude — a single botched tool call or missing \boxed marks the rollout a failure
- The puzzle: why does RL still deliver strong results, seemingly teaching behaviors the base model lacks?
The author flags it as obviously speculative but worth reading for anyone thinking about the fundamentals of RL training.
More from Research
- Q2D-Web benchmark debuts: 70K agent queries to evaluate retrievers across 190M web docs — antoine_chaffin · 2026-09-12
- Coding Is Not All You Need: CMU author argues GPT-6's robot tasks hit a world-model wall — ceciletamura · 2026-09-12
- Retrospectively Reverse-Engineering Apple's Neural Engine — zdw · 2026-09-12
- SentencePiece adds Auto-character Coverage as a clean alternative to Byte-Level BPE — prajdabre · 2026-09-12
- Hutter Prize opens an agent swarm challenge to hunt for the ultimate compression algorithm — mervenoyann · 2026-09-12
- DeepMind's Zahavy cites convex MDP paper in the 'reward is not the optimization target' debate — TZahavy · 2026-09-12