S3AC adds a stabilized actor-critic for LLM reasoning
Original title: S3AC: Stabilized, Selective, and Scalable Actor-Critic for LLM Reasoning
The paper reports gains from 1.5B to 397B parameters and stable single-rollout training at 35B.
S3AC is an actor-critic recipe for reinforcement learning with verifiable rewards on long chain-of-thought reasoning. A trained critic supplies token-specific advantages and prompt-specific baselines, and Difficulty-Aware Prompt Sampling uses that baseline plus outcome history to pick prompts before generation. The authors say it beats a GRPO-X variant on dense and mixture-of-experts models from 1.5B to 397B parameters. At 35B, they report that single-rollout training holds while tested critic-free baselines later decline.
Unconfirmed: Performance claims are the authors' reported results, not an independent evaluation.
- DAPS avoids offline difficulty initialization.
- Critic-free methods typically need grouped rollouts for baselines.
- Long-CoT critic training is limited by trajectory coverage and early value-head gradients.
- Reported comparison baseline is GRPO-X.
From the original article
1 min read at huggingface.co
Critic-free methods are widely used in reinforcement learning with verifiable rewards (RLVR) for long chain-of-thought (CoT) reasoning, avoiding difficult and costly critic training. Common alternatives lack token-level credit assignment and typically rely on grouped rollouts to estimate prompt-specific baselines.
By Shuo Shen, Guangran Cheng, Chengqi Lyu · huggingface.co
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