LaGO: Latent Action Guidance for Online Reinforcement Learning

2026-06-23 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, quick

Summary

LaGO, or Latent Action Guidance for Online Reinforcement Learning, is a novel framework that integrates large language models (LLMs) into reinforcement learning. Unlike previous approaches that use LLMs as direct, often unreliable, controllers for planning and sequential decision-making, LaGO employs a pretrained LLM as a latent action prior. This prior softly guides online policy optimization, enhancing performance without requiring precise action generation from the LLM. Experiments on both discrete-control (CLEVR-Robot) and continuous-control (Meta-World) benchmarks demonstrate significant improvements. LaGO increased the average success rate on CLEVR-Robot from 15.1% to 27.2% and on Meta-World from 2.7% to 15.2% compared to Vanilla PPO. Analysis further indicates that more powerful pretrained LLMs yield more effective guidance, underscoring the value of LLM knowledge in planning and online decision-making tasks.

Key takeaway

For Machine Learning Engineers developing online reinforcement learning agents, particularly in complex planning scenarios, you should explore integrating large language models as latent action priors. This LaGO approach offers a more reliable method than direct LLM control, significantly improving success rates and rewards. By utilizing the knowledge within stronger pretrained LLMs for soft guidance, you can enhance your agents' decision-making capabilities and overall performance on both discrete and continuous control tasks.

Key insights

Latent Action Guidance (LaGO) integrates pretrained LLMs as soft priors to enhance online reinforcement learning performance reliably.

Principles

• LLMs can act as effective latent action priors for RL.
• Stronger pretrained LLMs provide more effective guidance.
• Soft guidance improves online policy optimization.

Method

LaGO employs a pretrained LLM as a latent action prior to softly guide online policy optimization, moving away from direct LLM control for sequential decision-making.

In practice

• Apply pretrained LLMs as latent action priors in RL.
• Benchmark LLM-guided RL on discrete and continuous control.
• Prioritize stronger LLMs for more effective guidance.

Topics

• Reinforcement Learning
• Large Language Models
• Latent Action Guidance
• Online Policy Optimization
• Sequential Decision-Making
• Control Benchmarks

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Robotics Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.