BandPO: Bridging Trust Regions and Ratio Clipping via Probability-Aware Bounds for LLM Reinforcement Learning
#BandPO #reinforcement learning #trust regions #ratio clipping #LLM #probability-aware bounds #alignment #safety
📌 Key Takeaways
- BandPO introduces a new method for LLM reinforcement learning by combining trust regions and ratio clipping.
- It uses probability-aware bounds to improve training stability and performance.
- The approach aims to enhance alignment and safety in large language models.
- BandPO addresses limitations in existing reinforcement learning techniques for LLMs.
📖 Full Retelling
arXiv:2603.04918v1 Announce Type: cross
Abstract: Proximal constraints are fundamental to the stability of the Large Language Model reinforcement learning. While the canonical clipping mechanism in PPO serves as an efficient surrogate for trust regions, we identify a critical bottleneck: fixed bounds strictly constrain the upward update margin of low-probability actions, disproportionately suppressing high-advantage tail strategies and inducing rapid entropy collapse. To address this, we introd
🏷️ Themes
AI Research, Machine Learning
📚 Related People & Topics
Large language model
Type of machine learning model
A large language model (LLM) is a language model trained with self-supervised machine learning on a vast amount of text, designed for natural language processing tasks, especially language generation. The largest and most capable LLMs are generative pre-trained transformers (GPTs) that provide the c...
Entity Intersection Graph
Connections for Large language model:
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Artificial intelligence
3 shared
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Reinforcement learning
3 shared
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Educational technology
2 shared
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Benchmark
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OpenAI
2 shared
Mentioned Entities
Original Source
--> Computer Science > Machine Learning arXiv:2603.04918 [Submitted on 5 Mar 2026] Title: BandPO: Bridging Trust Regions and Ratio Clipping via Probability-Aware Bounds for LLM Reinforcement Learning Authors: Yuan Li , Bo Wang , Yufei Gao , Yuqian Yao , Xinyuan Wang , Zhangyue Yin , Xipeng Qiu View a PDF of the paper titled BandPO: Bridging Trust Regions and Ratio Clipping via Probability-Aware Bounds for LLM Reinforcement Learning, by Yuan Li and 6 other authors View PDF Abstract: Proximal constraints are fundamental to the stability of the Large Language Model reinforcement learning. While the canonical clipping mechanism in PPO serves as an efficient surrogate for trust regions, we identify a critical bottleneck: fixed bounds strictly constrain the upward update margin of low-probability actions, disproportionately suppressing high-advantage tail strategies and inducing rapid entropy collapse. To address this, we introduce Band-constrained Policy Optimization . BandPO replaces canonical clipping with Band, a unified theoretical operator that projects trust regions defined by f-divergences into dynamic, probability-aware clipping intervals. Theoretical analysis confirms that Band effectively resolves this exploration bottleneck. We formulate this mapping as a convex optimization problem, guaranteeing a globally optimal numerical solution while deriving closed-form solutions for specific divergences. Extensive experiments across diverse models and datasets demonstrate that BandPO consistently outperforms canonical clipping and Clip-Higher, while robustly mitigating entropy collapse. Comments: Code available at this https URL Subjects: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI) Cite as: arXiv:2603.04918 [cs.LG] (or arXiv:2603.04918v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2603.04918 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Yuan Li [ view email ] [v1] Thu, 5 Mar 2026 08:03:05 U...
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