Summary of Archer: Training Language Model Agents Via Hierarchical Multi-turn Rl, by Yifei Zhou et al.
ArCHer: Training Language Model Agents via Hierarchical Multi-Turn RL
by Yifei Zhou, Andrea Zanette, Jiayi Pan, Sergey Levine, Aviral Kumar
First submitted to arxiv on: 29 Feb 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 paper proposes a framework for building multi-turn reinforcement learning algorithms for large language models (LLMs) to improve their ability to make intelligent decisions in goal-directed decision-making tasks. The current single-turn RL methods for LLMs are limited in their ability to reason about past actions and seek information over multiple turns, which is crucial for agent tasks. To address this limitation, the authors develop a hierarchical RL approach that runs two RL algorithms in parallel: a high-level off-policy value-based algorithm and a low-level token policy algorithm. The proposed framework, Actor-Critic Framework with a Hierarchical Structure (ArCHer), can improve efficiency and performance on agent tasks by up to 100x over existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about finding a way to make big language models smarter at making decisions when they need to have multiple conversations or interactions. Right now, these models are only good at answering questions one time, but we want them to be able to have a conversation and make smart decisions along the way. The authors came up with a new way to train these models using something called reinforcement learning. This method is better than what we’re doing now because it lets the model learn from its past actions and ask for more information if it needs to. The new approach is also faster and more efficient, which means we can use even bigger models to get even better results. |
Keywords
* Artificial intelligence * Reinforcement learning * Token
