Controlling Large Language Model with Latent Actions
This addresses the challenge of defining action spaces for RL in LLMs, which is an incremental improvement for researchers and practitioners in AI and NLP.
This paper tackles the problem of adapting Large Language Models (LLMs) to downstream tasks using Reinforcement Learning (RL) by learning a compact latent action space to enhance controllability and exploration. The results show that CoLA with RL achieves a score of 42.4 on the math500 benchmark (vs. baseline 38.2), reduces computation time by half in certain tasks, and improves performance on agent-based tasks without degrading the LLM's capabilities.
Adapting Large Language Models (LLMs) to downstream tasks using Reinforcement Learning (RL) has proven to be an effective approach. However, LLMs do not inherently define the structure of an agent for RL training, particularly in terms of defining the action space. This paper studies learning a compact latent action space to enhance the controllability and exploration of RL for LLMs. We propose Controlling Large Language Models with Latent Actions (CoLA), a framework that integrates a latent action space into pre-trained LLMs. We apply CoLA to the Llama-3.1-8B model. Our experiments demonstrate that, compared to RL with token-level actions, CoLA's latent action enables greater semantic diversity in text generation. For enhancing downstream tasks, we show that CoLA with RL achieves a score of 42.4 on the math500 benchmark, surpassing the baseline score of 38.2, and reaches 68.2 when augmented with a Monte Carlo Tree Search variant. Furthermore, CoLA with RL consistently improves performance on agent-based tasks without degrading the pre-trained LLM's capabilities, unlike the baseline. Finally, CoLA reduces computation time by half in tasks involving enhanced thinking prompts for LLMs by RL. These results highlight CoLA's potential to advance RL-based adaptation of LLMs for downstream applications.