RLAR: An Agentic Reward System for Multi-task Reinforcement Learning on Large Language Models
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 al...
| Authors | Andrew Zhuoer Feng, Cunxiang Wang, Bosi Wen et al. |
|---|---|
| Primary category | cs.CL |
| Published | 28 February 2026 |
| Last updated at source | 28 February 2026 |
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