The challenges of developing instruction-following agents in grounded environments include sample efficiency and generalizability. Reinforcement learning and imitation learning are common techniques but can be costly and rely on trial and error or expert guidance. Language Feedback Models (LFMs) leverage large language models to provide sample-efficient policy improvement without continuous reliance on expensive models, offering interpretable feedback and significant policy adaptation gains in new environments. For more details, please refer to the original paper by Researchers from Microsoft Research and the University of Waterloo.
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Challenges in Developing Instruction-Following Agents
The challenges in developing instruction-following agents in grounded environments include sample efficiency and generalizability. These agents must learn effectively from a few demonstrations while performing successfully in new environments with novel instructions post-training.
Techniques for Instruction-Following Agents
Techniques like reinforcement learning and imitation learning are commonly used but often demand numerous trials or costly expert demonstrations due to their reliance on trial and error or expert guidance.
Language-Grounded Instruction Following
In language-grounded instruction following, agents receive instructions and partial observations in the environment, taking actions accordingly. Reinforcement learning involves receiving rewards, while imitation learning mimics expert actions.
Language Feedback Models (LFMs)
Researchers from Microsoft Research and the University of Waterloo have proposed Language Feedback Models (LFMs) for policy improvement in instruction. LFMs leverage large language models (LLMs) to provide feedback on agent behavior in grounded environments, aiding in identifying desirable actions. By distilling this feedback into a compact LFM, the technique enables sample-efficient and cost-effective policy improvement without continuous reliance on LLMs. LFMs generalize to new environments and offer interpretable feedback for human validation of imitation data.
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