Title: TROLL: Trust Regions Improve Reinforcement Learning for Large Language Models
Primary area: foundation or frontier models, including LLMs
Keywords: RL from verifiable rewards, Finetuning LLMs, Trust Regions

Abstract: On-policy 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 datasets, model families, and advantage-estimation methods, TROLL consistently outperforms PPO-like clipping in terms of training speed, stability, and final success rates.

TL;DR: Replacing PPO's clipping objective with more principled trust regions improves RL from verifiable rewards.