Debate as Reward: A Multi-Agent Reward System for Scientific Ideation via RL Post-Training
Large Language Models (LLMs) have demonstrated potential in automating scientific ideation, yet current approaches relying on iterative prompting or complex multi-agent architectures often suffer from hallucination or computational inefficiency. A critical bottleneck in applying Reinforcement Learning (RL) to this open-ended domain is reward hacking -- where models exploit imperfect evaluation proxies to maximize scores without producing genuine scientific innovation. To address these limitations, we propose an RL framework explicitly tailored for high-quality scientific idea generation. We propose the first multi-agent reward function designed to serve as a judge, decoupling methodologic...
| Authors | Moein Salimi, Babak Hosseini Mohtasham, Amin Aghakasiri et al. |
|---|---|
| Primary category | cs.AI |
| Published | 17 April 2026 |
| Last updated at source | 17 April 2026 |
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