| title | Beyond the Stability-Exploration Dilemma: Environmental Regularization for LLM Policy Optimization |
|---|---|
| authors | Xianlei Zhou, Xiangdi Meng, Yu He, Tianyu Qi, Shuyan Guan, Xianli Zhang, Jian Zhang, Xin Li, Qika Lin, Jun Liu |
| date | 2026-08-24 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2608.23311 |
| affiliation | alibaba |
| upvotes | 17 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | Policy optimization (PO) for Large Language Models faces a stability--exploration trade-off, currently mediated by an action-side Policy-KL regularizer. This puts practitioners in a double bind: keeping Policy-KL constrains response behavior and consumes the action-side exploration budget, while dropping it leaves the optimization without an explicit drift control. We argue for an alternative that breaks the dilemma by moving regularization to the input side. As training progresses, the distribution over training queries induced by the current policy drifts unchecked from its pre-RL reference distribution. Concretely, Environment-Regularized Policy Optimization (ERPO) introduces a Query-KL (QKL) term that bounds this query distribution shift, together with a dataset-static reference-derived per-query weight that biases each per-query update toward queries typical under the reference. The QKL gradient flows strictly through the query likelihood; the response score function used by policy-gradient estimators does not appear in the QKL term, so QKL exerts no direct gradient pressure on the response distribution---exploration is preserved. ERPO plugs into GRPO/PPO/REINFORCE-style pipelines without additional forward passes. On six mathematical reasoning benchmarks, ERPO replaces the standard Policy-KL regularizer while achieving effective control over query distribution drift, delivering stronger accuracy and substantially more stable behavior under high-temperature decoding and long-horizon training.Our source code are available at https://github.com/alibaba/ERPO |
cite
@misc{zhou2026beyond,
title={Beyond the Stability-Exploration Dilemma: Environmental Regularization for LLM Policy Optimization},
author={Xianlei Zhou and Xiangdi Meng and Yu He and Tianyu Qi and Shuyan Guan and Xianli Zhang and Jian Zhang and Xin Li and Qika Lin and Jun Liu},
year={2026},
eprint={2608.23311},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2608.23311}
}