TinyAstral: Transferring Self-Evolving Harnesses to Tiny Generalist Manipulation Agents
Overview
This paper presents TinyAstral, a two-stage approach that first improves a large teacher model's harness through recursive self-improvement (RSI) and then distills the enhanced capabilities into a compact student agent. On RoboTwin, this method raises the teacher's success rate from 33.4% to 63.6%, with the final 4B student reaching 62.8%. The harness gains transfer to other frozen planners without retraining, and the student outperforms open 4B agents and a 27B harness-RSI model while using about one-fifth of the teacher's accelerator time.
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- arXiv Robotics — research abstractsSignalTinyAstral: Transferring Self-Evolving Harnesses to Tiny Generalist Manipulation Agents
This paper presents TinyAstral, a two-stage approach that first improves a large teacher model's harness through recursive self-improvement (RSI) and then distills the enhanced capabilities into a compact student agent. On RoboTwin, this method raises the teacher's success rate from 33.4% to 63.6%, with the final 4B student reaching 62.8%. The harness gains transfer to other frozen planners without retraining, and the student outperforms open 4B agents and a 27B harness-RSI model while using about one-fifth of the teacher's accelerator time.
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