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[2510.00553] On Predictability of Reinforcement Learning Dynamics for Large Language Models
Summary
This article explores the predictability of reinforcement learning dynamics in large language models (LLMs), highlighting key properties of parameter updates and introducing a new acceleration framework, AlphaRL.
Why It Matters
Understanding the dynamics of reinforcement learning in LLMs is crucial for improving their training efficiency and performance. This research identifies significant properties of parameter updates that can lead to faster training methods, making it relevant for AI researchers and developers working with LLMs.
Key Takeaways
- Identifies Rank-1 Dominance in parameter updates, explaining its impact on reasoning improvements.
- Introduces Rank-1 Linear Dynamics, allowing predictions of model performance from early training checkpoints.
- Presents AlphaRL, a framework that accelerates training while maintaining high performance.
Computer Science > Machine Learning arXiv:2510.00553 (cs) [Submitted on 1 Oct 2025 (v1), last revised 22 Feb 2026 (this version, v3)] Title:On Predictability of Reinforcement Learning Dynamics for Large Language Models Authors:Yuchen Cai, Ding Cao, Xin Xu, Zijun Yao, Yuqing Huang, Zhenyu Tan, Benyi Zhang, Guangzhong Sun, Guiquan Liu, Junfeng Fang View a PDF of the paper titled On Predictability of Reinforcement Learning Dynamics for Large Language Models, by Yuchen Cai and 9 other authors View PDF HTML (experimental) Abstract:Recent advances in reasoning capabilities of large language models (LLMs) are largely driven by reinforcement learning (RL), yet the underlying parameter dynamics during RL training remain poorly understood. This work identifies two fundamental properties of RL-induced parameter updates in LLMs: (1) Rank-1 Dominance, where the top singular subspace of the parameter update matrix nearly fully determines reasoning improvements, recovering over 99\% of performance gains; and (2) Rank-1 Linear Dynamics, where this dominant subspace evolves linearly throughout training, enabling accurate prediction from early checkpoints. Extensive experiments across 8 LLMs and 7 algorithms validate the generalizability of these properties. More importantly, based on these findings, we propose AlphaRL, a plug-in acceleration framework that extrapolates the final parameter update using a short early training window, achieving up to 2.5 speedup while retaining \textgreater 9...
