Academic

RLAR: An Agentic Reward System for Multi-task Reinforcement Learning on Large Language Models

arXiv:2603.00724v1 Announce Type: new Abstract: Large language model alignment via reinforcement learning depends critically on reward function quality. However, static, domain-specific reward models are often costly to train and exhibit poor generalization in out-of-distribution scenarios encountered during RL iterations. We present RLAR (Reinforcement Learning from Agent Rewards), an agent-driven framework that dynamically assigns tailored reward functions to individual queries. Specifically, RLAR transforms reward acquisition into a dynamic tool synthesis and invocation task. It leverages LLM agents to autonomously retrieve optimal reward models from the Internet and synthesize programmatic verifiers through code generation. This allows the reward system to self-evolve with the shifting data distributions during training. Experimental results demonstrate that RLAR yields consistent performance gains ranging from 10 to 60 across mathematics, coding, translation, and dialogue tasks.

A
Andrew Zhuoer Feng, Cunxiang Wang, Bosi Wen, Yidong Wang, Yu Luo, Hongning Wang, Minlie Huang · · 1 min read · 2 views
#cs.CL

arXiv:2603.00724v1 Announce Type: new Abstract: Large language model alignment via reinforcement learning depends critically on reward function quality. However, static, domain-specific reward models are often costly to train and exhibit poor generalization in out-of-distribution scenarios encountered during RL iterations. We present RLAR (Reinforcement Learning from Agent Rewards), an agent-driven framework that dynamically assigns tailored reward functions to individual queries. Specifically, RLAR transforms reward acquisition into a dynamic tool synthesis and invocation task. It leverages LLM agents to autonomously retrieve optimal reward models from the Internet and synthesize programmatic verifiers through code generation. This allows the reward system to self-evolve with the shifting data distributions during training. Experimental results demonstrate that RLAR yields consistent performance gains ranging from 10 to 60 across mathematics, coding, translation, and dialogue tasks. On RewardBench-V2, RLAR significantly outperforms static baselines and approaches the performance upper bound, demonstrating superior generalization through dynamic reward orchestration. The data and code are available on this link: https://github.com/ZhuoerFeng/RLAR.

Executive Summary

This article presents RLAR (Reinforcement Learning from Agent Rewards), a novel framework for large language model alignment via reinforcement learning. RLAR addresses the limitations of traditional static reward models by dynamically assigning tailored reward functions to individual queries. Leveraging large language model agents, RLAR enables the synthesis of optimal reward models through code generation and retrieval from the internet. Experimental results demonstrate significant performance gains, outperforming static baselines and approaching the performance upper bound. This breakthrough has far-reaching implications for reinforcement learning and large language model alignment.

Key Points

  • ▸ RLAR presents a dynamic and agent-driven framework for reinforcement learning reward function assignment
  • ▸ Large language model agents autonomously retrieve and synthesize optimal reward models through code generation
  • ▸ Experimental results demonstrate consistent performance gains across various tasks and outperform static baselines

Merits

Strength in Dynamic Reward Orchestration

RLAR's ability to dynamically assign tailored reward functions to individual queries enables superior generalization and adaptability in out-of-distribution scenarios

Efficient Reward Model Retrieval and Synthesis

RLAR leverages large language model agents to retrieve and synthesize optimal reward models from the internet, reducing the computational burden and costs associated with traditional reward model training

Demerits

Dependence on Large Language Model Agent Capabilities

RLAR's performance relies heavily on the capabilities and accuracy of large language model agents, which may introduce additional complexity and potential biases in the reward assignment process

Potential for Overfitting to Query-Specific Reward Models

RLAR's focus on query-specific reward models may lead to overfitting, potentially compromising the generalizability of the reward system to unseen scenarios

Expert Commentary

RLAR constitutes a groundbreaking contribution to the field of reinforcement learning, showcasing the potential of dynamic and agent-driven reward systems for large language model alignment. However, its success also underscores the importance of addressing the associated challenges, such as the dependence on large language model agent capabilities and the potential for overfitting. As RLAR continues to evolve, it will be essential to monitor its performance and adaptability in diverse applications, ensuring that the benefits of dynamic reward orchestration are balanced with the need for governance and accountability.

Recommendations

  • ✓ Further research is needed to investigate the scalability and generalizability of RLAR in various domains and tasks, particularly in scenarios where query-specific reward models may not be sufficient
  • ✓ Developing more sophisticated large language model agents that can effectively retrieve and synthesize optimal reward models will be crucial for the widespread adoption of RLAR

Sources

Related Articles