Co-ReAct: Rubrics as Step-Level Collaborators for ReAct Agents
📰 ArXiv cs.AI
Learn how Co-ReAct uses rubrics as step-level collaborators to improve ReAct agents' performance in search-intensive tasks
Action Steps
- Implement Co-ReAct architecture to integrate rubrics as step-level collaborators
- Train ReAct agents using Co-ReAct to improve their internal judgment and decision-making
- Evaluate the performance of Co-ReAct agents in search-intensive tasks and compare with existing approaches
- Use rubrics as external quality signals to guide the agents' actions and decisions
- Analyze the impact of Co-ReAct on the agents' trajectories and adjust the rubrics accordingly
Who Needs to Know This
Researchers and developers working on ReAct agents and multi-step reasoning tasks can benefit from this approach to improve their agents' performance and decision-making
Key Insight
💡 Co-ReAct uses rubrics as step-level collaborators to guide ReAct agents' actions and decisions, improving their performance in search-intensive tasks
Share This
️ Improve ReAct agents with Co-ReAct! Rubrics as step-level collaborators enhance decision-making in search-intensive tasks #AI #ReAct
Key Takeaways
Learn how Co-ReAct uses rubrics as step-level collaborators to improve ReAct agents' performance in search-intensive tasks
Full Article
Title: Co-ReAct: Rubrics as Step-Level Collaborators for ReAct Agents
Abstract:
arXiv:2605.23590v1 Announce Type: new Abstract: ReAct-style agents for search-intensive, multi-step reasoning tasks rely largely on their own internal judgment to decide what evidence to seek, which reasoning or action step to take next, and when to stop, often producing shallow, redundant, or poorly targeted trajectories. Prior work has explored rubrics as external quality signals, but existing uses are mostly evaluative rather than action-guiding: rubrics typically serve as training-time rewar
Abstract:
arXiv:2605.23590v1 Announce Type: new Abstract: ReAct-style agents for search-intensive, multi-step reasoning tasks rely largely on their own internal judgment to decide what evidence to seek, which reasoning or action step to take next, and when to stop, often producing shallow, redundant, or poorly targeted trajectories. Prior work has explored rubrics as external quality signals, but existing uses are mostly evaluative rather than action-guiding: rubrics typically serve as training-time rewar
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