Game-Theoretic Approach to RL Fine-Tuning in Language Models
A new paper on arXiv (2607.26358) proposes a game-theoretic framework for reinforcement learning fine-tuning of language models. The standard KL-regularized RL objective lacks a principled way to set the regularization coefficient, often relying on heuristics or hyperparameter search. The authors model the process as a sequential game where an agent maximizes cumulative reward while a monitor tests for policy deviations. This approach gives the trade-off an explicit statistical interpretation, potentially reducing training overhead and improving reward-retention balance.
Key facts
- arXiv paper 2607.26358 proposes game-theoretic framework for RL fine-tuning
- Standard KL-regularized RL objective lacks principled regularization coefficient setting
- Coefficient is typically chosen heuristically or via hyperparameter search
- Sequential game: agent maximizes reward, monitor tests for policy deviations
- Framework provides explicit statistical interpretation of trade-off
- Aims to reduce training cost overhead and improve reward-retention trade-offs
- Paper type: cross (cross-listed)
- Published on arXiv
Entities
Institutions
- arXiv