PACT: From Credit Assignment to Critic Alignment
PACT realigns actor-critic training and beats GRPO and PPO on math reasoning and SWE-bench Verified.
The paper argues that Completeness, Prefix Consistency, and Neutrality uniquely define token-level credit in LLM reinforcement learning, and uses that result to relate On-Policy Distillation and response-level RLOO to the same policy-gradient signal. It also shows that critic error in Generalized Advantage Estimation can become comparable to the true credit. Policy Aligned Critic Training (PACT) applies an actor-then-critic update with importance sampling so the critic matches the updated policy. PACT reports 72.87% average accuracy on four math benchmarks, beating GRPO by 8.80 points and PPO by 13.16, and a 67.4% pass rate on SWE-bench Verified.
- Three regularity conditions uniquely determine token-level credit.
- PACT updates the actor first, then corrects critic training with importance sampling.
- Average math accuracy is 72.87%, 8.80 points above GRPO and 13.16 above PPO.
- SWE-bench Verified pass rate is 67.4%, ahead of PPO, GRPO, and SAO.
From the original article
1 min read at huggingface.co
Reinforcement learning has become a central component of large language model (LLM) post-training, yet token-level credit lacks a generally accepted mathematical definition, leaving its relationship to commonly used training signals unclear. We formulate three regularity conditions, namely Completeness, Prefix Consistency, and Neutrality, and prove that they uniquely determine token-level credit.
By Jiayan Fu, Hang Xu, Yong Zhang · huggingface.co
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