![[2510.03817] TROLL: Trust Regions improve Reinforcement Learning for Large Language Models](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2510.03817] TROLL: Trust Regions improve Reinforcement Learning for Large Language Models
Summary
The paper presents TROLL, a novel method that replaces traditional PPO-like clipping in reinforcement learning with a trust region optimization approach, enhancing training speed and stability for large language models.
Why It Matters
As reinforcement learning continues to be integral in fine-tuning large language models, TROLL addresses limitations of existing methods, potentially leading to more efficient and effective training processes. This advancement can significantly impact applications in AI where stability and performance are critical.
Key Takeaways
- TROLL replaces clipping with a discrete differentiable trust region projection.
- The method improves training speed and stability for large language models.
- TROLL maintains model inference behavior while enhancing performance.
- It consistently outperforms traditional PPO-like clipping across various tasks.
- The approach balances computational cost with projection effectiveness.
Computer Science > Machine Learning arXiv:2510.03817 (cs) [Submitted on 4 Oct 2025 (v1), last revised 23 Feb 2026 (this version, v3)] Title:TROLL: Trust Regions improve Reinforcement Learning for Large Language Models Authors:Philipp Becker, Niklas Freymuth, Serge Thilges, Fabian Otto, Gerhard Neumann View a PDF of the paper titled TROLL: Trust Regions improve Reinforcement Learning for Large Language Models, by Philipp Becker and 4 other authors View PDF Abstract:Reinforcement Learning (RL) with PPO-like clip objectives has become the standard choice for reward-based fine-tuning of large language models (LLMs). Although recent work has explored improved estimators of advantages and normalization, the clipping mechanism itself has remained untouched. Originally introduced as a proxy for principled KL-based trust regions, clipping is a crude approximation that often causes unstable updates and suboptimal performance. We replace the clip objective with a novel discrete differentiable trust region projection, which provides principled token-level KL constraints. The projection operates on a sparse subset of the model's most important token logits to balance computational cost and projection effectiveness. Our approach, Trust Region Optimization for Large Language models (TROLL), serves as a direct replacement for PPO-like clipping during training and does not alter the model's inference behavior. Across mathematical reasoning and code generation tasks, model families, as well...
