Guava distills Astra robot skills into a 4B model
The open-source Guava framework fine-tuned Qwen3.5 into Guava-4B using about 2K simulation trajectories, reaching 90% real-world success with 7.1x lower latency.
🤖 Frontier models like GPT 6 Astra are getting surprisingly good at controlling robots. The results are exciting, but deploying frontier models on robots comes with substantial inference cost and latency. Can we distill these capabilities into compact models that run locally? We open-source 🥑Guava — a framework for distilling frontier models into compact robot agents, using purely simulation interaction data, no real-world data required! With only ~2K simulation trajectories, we finetuned Qwen3.5 into Guava-4B, with: 🤖 90.0% real-world success vs. 93.3% for GPT-5.4 ⚡ 7.1× lower model-call latency with local inference 🌎 Sim-to-real transfer without real-world fine-tuning 🧠 Generalization to unseen tasks and object configurations 🔄 Closed-loop recovery from execution failures Paper + code + project page: https://guava-harness.github.io/ 🎥 See Guava in action below. Joint efforts from @haowenssr, @xiruili7_li, @ShaoxiongYao, Peng Shi, @zhoutianyi, @jbhuang0604 , @furongh and @maojiayuan.