Reproduce “Verifiable Environments Are LEGO Bricks: Recursive Composition for Reasoning Generalization” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Hao Xiang, Qiaoyu Tang, Le Yu, Yaojie Lu, Xianpei Han, Ben He, Le Sun, Bowen Yu and 3 more
Researchers from the University of Chinese Academy of Sciences and Alibaba Group developed RACES, a framework that recursively composes verifiable environments, enabling combinatorial scaling of training data. This approach significantly improves Large Language Model reasoning generalization and environment utilization efficiency by fostering more robust and transferable reasoning patterns.
Key insights
- Composite environments provide a more effective training signal than isolated ones, consistently improving LLM reasoning generalization by fostering skills like multi-step transformation and state maintenance.
- RACES significantly increases the utility of each base environment, allowing comparable or superior performance with a fraction of the base environments compared to training on a larger individual pool.
- Composition size acts as a controllable variable for task difficulty, enabling systematic curriculum learning where moderate-to-large depths are beneficial but excessive depths can hinder optimization.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.