Reward Function Design for Open-Ended Scientific Discovery

Develop a formal, computationally evaluable reward function for training reinforcement learning–based large language model (LLM) scientific agents that quantifies novelty, impact, and reproducibility in open-ended scientific discovery tasks, enabling effective reward-guided learning when definitive final signals are sparse or unavailable.

Background

The paper argues that applying agentic reinforcement learning (RL) methods to scientific discovery faces unique obstacles, especially in defining meaningful reward signals. Unlike math or web tasks where correctness or completion provides clear rewards, scientific discovery is inherently open-ended and long-horizon, with sparse outcomes and ambiguous success criteria.

Within this context, the authors highlight that measuring novelty, impact, and reproducibility is essential yet difficult to encode as a computable reward. This makes RL optimization of scientific agents challenging, positioning the reward design question as central to achieving general-purpose autonomy in science.