RODS
RL-powered data synthesis for training multi-turn tool-use agents at capability boundaries
About
RODS (Reward-Driven Online Data Synthesis) is a research framework that addresses data depletion in reinforcement learning for tool-using agents. It continuously generates high-quality training samples at the agent's capability frontier, improving sample efficiency by 2-3x compared to static datasets. Designed for researchers building multi-turn agents that need to learn complex tool interactions through RL, RODS dynamically adapts training data as agents improve.
Details
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Tags
Quick Info
- Organization
- Research Team (Fang et al.)
- Pricing
- open-source
- Free Tier
- Yes
- Updated
- Jun 18, 2026
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