Credit assignment in multi-turn agent reinforcement learning operates at two levels: assigning trajectory-level credit to actions and distributing each action's credit across its tokens. In this paper, we introduce FACTOR, which separates these decisions. FACTOR uses checkpoint-calibrated TD residuals to assign per-action credits that telescope to the trajectory advantage, and feedback-conditioned teacher-student likelihood gaps to allocate each credit across the realized action tokens. Per-action normalization preserves the action-average coefficient and prevents token-level sign flips. We pair this construction with an action-mean reduction, removing the implicit dependence of an action's scalar surrogate weight on its token length. At the behavior policy and before clipping, each action's inner action-mean surrogate equals its TD credit. FACTOR consistently improves over competitive baselines across ALFWorld, WebShop, and ScienceWorld, with every environment-seed comparison favoring FACTOR and the largest gains emerging on the longest-horizon environment. The same hyperparameters transfer without retuning to a larger backbone and to a different model family. Ablations identify TD action credit as the dominant driver of the improvement, with hindsight token allocation contributing complementary gains.
Li-Chao Ma, Yang Sun, Shuai Zhao et al.· 0 citations
It is shown that policy entropy bounds both the policy gradient and probability update norms; consequently, entropy collapse effectively stops reward signal backpropagation, preventing further policy learning regardless of data quality.
Cong Qin, Jiaye Lin, Xiaoliang Fu et al.· Proceedings of the 32nd ACM...· 0 citations
Reinforcement Learning with Verifiable Rewards (RLVR) has become a standard approach for improving complex reasoning in Large Language Models (LLMs). However, entropy collapse remains a critical bottleneck, often leading to performance degradation. While typically attributed to reduced exploration, we identify a more severe failure mode: update deadlock. We show that policy entropy bounds both the policy gradient and probability update norms; consequently, entropy collapse effectively stops reward signal backpropagation, preventing further policy learning regardless of data quality. To analyze this, we develop a micro-dynamics framework based on single-step logit updates. Our results indicate that entropy evolution depends on the interaction between advantage signals and the log-probability residual relative to its expectation, offering a unified perspective on existing interventions. Based on these findings, we propose ANCHOR, an algorithm designed to resolve deadlocks and maintain stability. ANCHOR uses a dual mechanism: it dynamically anchors the entropy decay of positive samples to prevent premature saturation, and uses entropy variations to stabilize updates for negative samples. Extensive experiments on reasoning benchmarks show that ANCHOR significantly outperforms strong baselines. Unlike heuristic methods that induce verbosity to mitigate collapse, ANCHOR promotes concise reasoning paths, improving both stability and efficiency.
Cong Qin, Jiaye Lin, Xiaoliang Fu et al.· Proceedings of the 32nd ACM...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
