Summary of True Knowledge Comes From Practice: Aligning Llms with Embodied Environments Via Reinforcement Learning, by Weihao Tan et al.
True Knowledge Comes from Practice: Aligning LLMs with Embodied Environments via Reinforcement Learning
by Weihao Tan, Wentao Zhang, Shanqi Liu, Longtao Zheng, Xinrun Wang, Bo An
First submitted to arxiv on: 25 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed TWOSOME framework combines large language models (LLMs) with reinforcement learning (RL) agents to efficiently interact and align with embodied environments. The approach deploys LLMs as decision-making agents, querying joint probabilities of valid actions to form behavior policies. To enhance policy stability and robustness, two normalization methods and four prompt design principles are proposed. A novel parameter-efficient training architecture is designed, featuring a shared frozen LLM equipped with low-rank adapters (LoRA) updated by PPO. Extensive experiments demonstrate TWOSOME’s superior sample efficiency, performance, and generalization ability compared to conventional RL and prompt tuning methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary TWOSOME is a new way to use large language models to make decisions in complex environments. It combines the language model with reinforcement learning, which helps the agent learn from its experiences. The framework asks the language model to suggest possible actions and then uses those suggestions to improve its decision-making. This approach shows promise for solving simple decision-making tasks more efficiently than current methods. |
Keywords
* Artificial intelligence * Generalization * Language model * Lora * Parameter efficient * Prompt * Reinforcement learning
