![[2602.03195] Reinforcement Learning with Promising Tokens for Large Language Models](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.03195] Reinforcement Learning with Promising Tokens for Large Language Models
Summary
This article presents a novel framework called Reinforcement Learning with Promising Tokens (RLPT) designed to optimize large language models by refining the action space, improving training stability and efficiency.
Why It Matters
As large language models become increasingly integral to AI applications, optimizing their performance through effective training methods is crucial. The RLPT framework addresses the challenges posed by irrelevant tokens in the action space, enhancing the decision-making capabilities of these models. This research has implications for various AI applications, including coding and reasoning tasks.
Key Takeaways
- RLPT mitigates the action space issue by focusing on a refined set of promising tokens.
- The framework improves sample efficiency and stabilizes the training process.
- Empirical results show RLPT outperforms standard reinforcement learning baselines.
- The approach effectively integrates across different model sizes and RL algorithms.
- Valid reasoning paths are shown to concentrate within a low-rank subspace, enhancing model performance.
Computer Science > Machine Learning arXiv:2602.03195 (cs) [Submitted on 3 Feb 2026 (v1), last revised 14 Feb 2026 (this version, v2)] Title:Reinforcement Learning with Promising Tokens for Large Language Models Authors:Jing-Cheng Pang, Liang Lu, Xian Tang, Kun Jiang, Sijie Wu, Kai Zhang, Xubin Li View a PDF of the paper titled Reinforcement Learning with Promising Tokens for Large Language Models, by Jing-Cheng Pang and 6 other authors View PDF HTML (experimental) Abstract:Reinforcement learning (RL) has emerged as a key paradigm for aligning and optimizing large language models (LLMs). Standard approaches treat the LLM as the policy and apply RL directly over the full vocabulary space. However, this formulation includes the massive tail of contextually irrelevant tokens in the action space, which could distract the policy from focusing on decision-making among the truly reasonable tokens. In this work, we verify that valid reasoning paths could inherently concentrate within a low-rank subspace. Based on this insight, we introduce Reinforcement Learning with Promising Tokens (RLPT), a framework that mitigates the action space issue by decoupling strategic decision-making from token generation. Specifically, RLPT leverages the semantic priors of the base model to identify a dynamic set of promising tokens and constrains policy optimization exclusively to this refined subset via masking. Theoretical analysis and empirical results demonstrate that RLPT effectively reduces gra...
