KLPO：面向LLM智能体的免critic策略优化
arXiv 新论文《On KL-Regularized Policy Optimization》（作者 Yifan Zhang，2026年10月6日提交）提出 KLPO 框架，将 KL 正则项锚定在采样器一侧，使大语言模型智能体的异步强化学习可以直接使用陈旧 checkpoint 产生的轨迹，且不需要重要性权重。论文称正则化改进步存在闭式 Gibbs 解，并证明在线性策略镜像下降目标下，梯度可由终端回报直接计算、无需 critic 网络，每个 prompt 只需一条 rollout。论文还证明对 KL 项的独立蒙特卡洛估计能保持梯度无偏，并指出 SPPO、GPO、REBEL 与 BPO 均为 KLPO 的特例。
为什么重要：若该结论经得起检验，有望简化 LLM 智能体的异步强化学习训练流程，去掉 critic 网络与逐 prompt 的多回复采样，但文中所述均为理论推导，尚未见实验验证。
Computer Science > Machine Learning
arXiv:2610.08963 (cs)
[Submitted on 6 Oct 2026]
Title:On KL-Regularized Policy Optimization
Authors:Yifan Zhang
View a PDF of the paper titled On KL-Regularized Policy Optimization, by Yifan Zhang
View PDFAbstract:Asynchronous reinforcement learning (RL) for large language model (LLM) agents trains one policy on trajectories generated by another: rollouts come from stale checkpoints, and the inference engine's probabilities differ from the trainer's even at identical parameters. Standard remedies either clip importance ratios, which biases the update, or, as in GRPO, sample a group of responses per prompt, which is costly when episodes are long. We propose KL-Regularized Policy Optimization (KLPO), a framework that anchors the KL regularizer at the sampler. The regularized improvement step then has a closed-form Gibbs solution, and KLPO fits its log-ratio optimality condition by least squares on the sampler's own trajectories, so the sampler probability enters through a log-ratio and no importance weights are needed. Profiling out the regression intercept replaces the intractable log-partition function with the signal's sampler mean plus a sampler-to-trainer KL divergence. For token-level policy mirror descent targets, we show that the resulting gradient can be computed from terminal returns without a critic, via sampler-centered scores or a single trajectory residual, even under stochastic tool outputs. We further prove that independent Monte Carlo estimates of the KL term keep these gradients unbiased, derive the exact KL gap of cheaper top-$K$ and binary approximations, and show that SPPO, GPO, REBEL, and BPO arise as special cases of KLPO. The result is a critic-free update that uses one rollout per prompt and requires neither a learned normalizer nor a group of responses.
| Comments: | Project Page: this https URL |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.08963 [cs.LG] |
| (or arXiv:2610.08963v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.08963 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Yifan Zhang [view email][v1] Tue, 6 Oct 2026 18:30:42 UTC (60 KB)
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