Beyond Scripted Search: Sample-Efficient Reward Discovery via Agentic Black-box Optimization.

arXiv:2609.32394v1 Announce Type: new Abstract: Designing dense reward functions for low-level reinforcement learning (RL) control remains difficult. Recent work uses large language models (LLMs) to iteratively generate and refine reward functions using policy