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We can teach LLMs to write better robot code through natural language feedback. But can LLMs remember what they were taught and improve their teachability over time? Introducing our latest work, Learning to Learn Faster from Human Feedback with Language Model Predictive Control

Jacky Liang

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86,549 görüntüleme • 2 yıl önce •via X (Twitter)

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TL;DR We improve LLMs’ in-context teachability by fine-tuning them to model human-robot interactions. Here’s an example of robot teaching sessions before and after finetuning. Before, the LLM requires many corrections to do a high-five; after, the model gets there much faster

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Given a dataset of these chat-based teaching sessions, how do we improve LLMs’ teachability (number of chat turns it takes to reach task success)? We want to improve teachability not just on train tasks, but also on test tasks and test embodiments.

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Instead of finetuning to directly output final answers, we propose Language Model Predictive Control: 1) Finetune the LLM to predict the entire chat session 2) During inference: sample rollouts of future chat sessions, return the first response of the shortest successful chat

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Compared to retrieval (RAG) and directly predict the answer (LMPC-Skip), our method (LMPC-Rollouts) achieves the highest improvement in teachability over the base model (PaLM 2-S) With 1 chat turn, LMPC-Skip does the best, but LMPC-Rollouts improves more with corrective feedback

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On test tasks, LMPC-Rollouts have the highest success rate when the chat has 2+ turns; it also has the highest good rating rate (if human teachers rated each chat response positively).

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While we only fine-tune on 3 embodiments (robot dog, mobile manipulator, aloha), we also see improvements on test embodiments (bimanual kuka, kuka+hand), both have different robot APIs and tasks

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Our method enables better teaching on real robots too! Here’s an example with the robot dog…

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…and here’s another example for our mobile manipulation robot

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See more details in our paper, and videos and demo on our website:

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This was a huge collaboration from many folks at Google DeepMind Robotics. I learned a ton from everyone and am super grateful for the amazing teamwork! Special thanks to @xf1280 @Stacormed @andyzeng_! Big shoutouts to our incredible collaborators: @montseglz @JMarakiii …

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… @bauzavillalonga Matthew Bennice @AlexBewleyAI @AdilDostmohamed @ChuyuanFu @NimTheCoder Marissa Giustina @keerthanpg @lqh20 Jan Humplik, Jasmine Hsu, Nikhil Joshi, Ben Jyenis, Chase Kew, @SeanKirmani Edward Lee, @kuanghueilee, Assaf Hurwitz Michaely, Joss Moore, Ken Oslund, …

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…, Dushyant Rao, @allenzren Baruch Tabanpour @QuanVng @ayzwah @xiao_ted Ying Xu, Vincent Zhuang, as well as our incredible advising leads: Peng Xu, Erik Frey, Ken Caluwaerts, Tingnan Zhang, @brian_ichter @JonathanTompson @leilatakayama Vincent Vanhoucke @IzhakShafran …

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…Maja Mataric @DorsaSadigh Nicolas Heess @Kanishka_Rao Nik Stewart, Jie Tan, Carolina Parada Thanks everyone!!

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