A Practitioner's Guide to Multi-turn Agentic Reinforcement Learning

A recent study has introduced a systematic evaluation framework for aligning large language models (LLMs) with diverse human preferences in federated learning environments. This framework assesses the trade-off between alignment quality and fairness using various aggregation strategies for human preferences, including a novel adaptive scheme that adjusts preference weights based on historical performance.

A new study introduces SPEAR, a self-imitation learning approach designed to enhance the exploration-exploitation balance in reinforcement learning for large language models (LLMs). This method aims to improve the stability of RL training by utilizing the agent's own experiences to guide policy entropy adjustments, addressing challenges associated with traditional exploration techniques.

A new paper proposes a novel approach to decoding-based regression by utilizing Reinforcement Learning (RL) to enhance numerical prediction accuracy. This method addresses the limitations of traditional token-level objectives, which often misalign with continuous numerical values, thereby improving the precision and generalization of predictions.

A-3PO, a new approach to asynchronous reinforcement learning (RL), has been introduced to enhance the training of large language models (LLMs) by reducing computational overhead. This method approximates the proximal policy through interpolation, eliminating the need for an extra forward pass, which traditionally slows down training. As a result, A-3PO achieves an 18% reduction in training time while maintaining performance levels comparable to existing algorithms.

A systematic comparison of three Reinforcement Learning algorithms—PPO, GRPO, and DAPO—has been conducted to enhance reasoning capabilities in large language models (LLMs). The study involved fine-tuning models on the Countdown Game and evaluating their performance on various reasoning benchmarks, revealing that RL-trained models generally outperform their base counterparts, albeit with varying degrees of improvement across benchmarks.

The Parent-Guided Semantic Reward Model (PGSRM) has been introduced as a novel framework for reinforcement learning in transformer language models, utilizing cosine similarity between output embeddings of parent and child models to generate dense semantic rewards without requiring human annotations or additional training. This approach has been tested across five language tasks, demonstrating smoother reward improvements and more stable dynamics compared to traditional binary reward systems.