Advances in Reinforcement Learning for Large Language Models

The field of reinforcement learning for large language models is moving towards more efficient and effective methods for training and exploration. Recent developments have focused on addressing the challenges of sparse reward signals, improving policy optimization, and enhancing exploration strategies. Notably, researchers are exploring new approaches to policy gradients, such as entropy-modulated policy gradients and flow-based methods, which aim to improve the stability and diversity of learned policies. Additionally, there is a growing interest in leveraging intrinsic motivation and curiosity-driven exploration to guide the learning process. These advancements have the potential to significantly improve the performance and generalization of large language models in various tasks and domains.

Some noteworthy papers in this area include: Clip Your Sequences Fairly, which proposes a method for enforcing length fairness in sequence-level reinforcement learning. Harnessing Uncertainty, which introduces a framework for re-calibrating the learning signal based on step-wise uncertainty and the final task outcome. TDRM, which presents a method for learning smoother and more reliable reward models by minimizing temporal differences during training. EVOL-RL, which proposes a simple rule that couples stability with variation under a label-free setting, preventing collapse and maintaining longer and more informative chains of thought. FlowRL, which transforms scalar rewards into a normalized target distribution and minimizes the reverse KL divergence between the policy and the target distribution, promoting diverse exploration and generalizable reasoning trajectories.

Sources

Clip Your Sequences Fairly: Enforcing Length Fairness for Sequence-Level RL

Harnessing Uncertainty: Entropy-Modulated Policy Gradients for Long-Horizon LLM Agents

Tree-OPO: Off-policy Monte Carlo Tree-Guided Advantage Optimization for Multistep Reasoning

CDE: Curiosity-Driven Exploration for Efficient Reinforcement Learning in Large Language Models

Inpainting-Guided Policy Optimization for Diffusion Large Language Models

Single-stream Policy Optimization

Online Learning of Deceptive Policies under Intermittent Observation

TDRM: Smooth Reward Models with Temporal Difference for LLM RL and Inference

Evolving Language Models without Labels: Majority Drives Selection, Novelty Promotes Variation

FlowRL: Matching Reward Distributions for LLM Reasoning

Built with on top of