Reproduce “Trade-R1: Bridging Verifiable Rewards to Stochastic Environments via Process-Level Reasoning Verification” on OpenResearch

About this paper

Rui Sun, Yifan Sun, Sheng Xu, Li Zhao, Jing Li, Daxin Jiang, Cheng Hua, Zuo Bai

The Trade-R1 framework enables Large Language Models to make robust financial decisions in stochastic environments by integrating process-level reasoning verification. It introduces a dynamic semantic reward and a two-stage verification method, achieving superior cross-market generalization and high reasoning quality while mitigating reward hacking.

Key insights

• Directly optimizing LLMs with noisy market rewards without process-level verification leads to reward hacking, significant reasoning degradation, and poor generalization to unseen markets.
• Dynamic and asymmetric integration of reasoning quality into the reward signal (DSR) is crucial for fostering transferable, causally grounded reasoning, preventing penalty evasion, and achieving robust cross-market generalization.
• Efficient and accurate verification of reasoning over long contexts is achieved by a two-stage method that decouples evidence retrieval from logical evaluation, overcoming attention dilution and computational bottlenecks.

Summary by alphaXiv.