Across coding, web, and tool-use tasks, language-model agents are now routinely trained with the same recipe: supervised fine-tuning (SFT) to learn the interface, then multi-turn reinforcement learnin
CaPtain trains small open vision-language models via supervised fine-tuning and multi-turn reinforcement learning with a physical critic to perform robot manipulation through code, achieving strong results without frontier models or vision-language-action backbones.
Across coding, web, and tool-use tasks, language-model agents are now routinely trained with the same recipe: supervised fine-tuning (SFT) to learn the interface, then multi-turn reinforcement learning (RL) to master it. Code-as-policy (CaP) casts robot manipulation in the same form, as the policy writes code against robot APIs and reads back the results, so the same recipe should apply. Yet existing CaP agents sidestep training: they either prompt closed frontier models or delegate manipulation to pre-trained vision-language-action models (VLAs) and high-level skills, while small open vision-language models (VLMs) fail outright. We present CaPtain, an open post-training pipeline that…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
CaPtain: A Pipeline for Post-Training Code-as-Policy Robot Agents was first seen on Hugging Face · Daily papers on Nov 2, 2026. Since then it has appeared in 13 checks on 2 different days. Its best position was #13 of 30, reached on Nov 2, 11:28 UTC. It dropped off the list after Nov 3, 07:28 UTC.
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Not at the moment. It was last on the list on Nov 3, 07:28 UTC, after 2 days of trending.
#13 on Hugging Face · Daily papers, on Nov 2, 11:28 UTC. Trendoday checks this list every 120 minutes.
It was first seen on Nov 2, 2026 and has been on the list on 2 days (13 checks) since.