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AI Research
arXiv CS.AI·May 25·8 min read
Co-ReAct: Rubrics as Step-Level Collaborators for ReAct Agents
TL;DR
ReAct agents (systems that reason and act iteratively) struggle with poor decision-making during multi-step tasks because they lack external guidance on what evidence to seek. Co-ReAct injects step-level rubrics (quality criteria) into the agent's reasoning at each decision point to guide action selection.
✦ Why It Matters
Engineers can inject step-level guidance into reasoning agents to improve decision quality without retraining the base model.
Key Takeaways
- Integrate Co-ReAct for improved decision-making in AI agents.
- Utilize the rubric generator to enhance evidence gathering.
- Test Co-ReAct on your existing models for better outcomes.
- Leverage step-level guidance to optimize reasoning processes.
How It Works
Co-ReAct employs a rubric-guided action-selection framework where rubrics are injected into the agent's context at each decision step. This provides specific guidance on what evidence to seek and how to evaluate reasoning, improving the agent's ability to make informed decisions.