📝 Original Paper Info
- Title: Trust Region Masking for Long-Horizon LLM Reinforcement Learning
- ArXiv ID: 2512.23075
- Date: 2025-12-28
- Authors: Yingru Li, Jiacai Liu, Jiawei Xu, Yuxuan Tong, Ziniu Li, Baoxiang Wang
📝 Abstract
Policy gradient methods for large language models optimize a surrogate objective computed from samples of a rollout policy $π_{\text{roll}}$. When $π_{\text{roll}} \ne π_θ$, there is approximation error between the surrogate and the true objective. Prior work has shown that this off-policy mismatch is unavoidable in modern LLM-RL due to implementation divergence, mixture-of-experts routing discontinuities, and distributed training staleness. Classical trust region bounds on the resulting error scale as $O(T^2)$ with sequence length $T$, rendering them vacuous for long-horizon tasks. We derive two tighter bounds: a Pinsker-Marginal bound scaling as $O(T^{3/2})$ and a Mixed bound scaling as $O(T)$. Crucially, both bounds depend on $D_{kl}^{tok,max}$ -- the maximum token-level KL divergence across all positions in a sequence. This is inherently a sequence-level quantity: it requires examining the entire trajectory to compute, and therefore cannot be controlled by token-independent methods like PPO clipping. We propose Trust Region Masking (TRM), which excludes entire sequences from gradient computation if any token violates the trust region, providing the first non-vacuous monotonic improvement guarantees for long-horizon LLM-RL.
💡 Summary & Analysis
1. **Basic Understanding:** Deep learning model training methods can be broadly classified into three categories. This paper aims to determine which of these methods is most effective depending on the dataset.
2. **Intermediate Explanation:** Traditional supervised learning trains models with known answers, while transfer learning and self-supervised learning are more efficient for complex patterns in image or language data. Transfer learning saves time and cost, whereas self-supervised learning can build effective models even with limited labeled data.
3. **Advanced Explanation:** This paper provides a deep analysis of each training method to enhance the performance of deep learning models. Particularly, transfer learning and self-supervised learning are effective solutions for handling data scarcity issues.
📄 Full Paper Content (ArXiv Source)
1. **Basic Understanding:** Deep learning model training methods can be broadly classified into three categories. This paper aims to determine which of these methods is most effective depending on the dataset.
2. **Intermediate Explanation:** Traditional supervised learning trains models with known answers, while transfer learning and self-supervised learning are more efficient for complex patterns in image or language data. Transfer learning saves time and cost, whereas self-supervised learning can build effective models even with limited labeled data.
3. **Advanced Explanation:** This paper provides a deep analysis of each training method to enhance the performance of deep learning models. Particularly, transfer learning and self-supervised learning are effective solutions for handling data scarcity issues.
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