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LaGO framework uses LLMs to improve online reinforcement learning · 2 sources tracked

By PulseAugur Editorial · [2 sources] ·

Researchers have developed LaGO, a framework that leverages large language models (LLMs) as a latent action prior for online reinforcement learning. Instead of using LLMs as direct controllers, LaGO softly guides policy optimization. Experiments on CLEVR-Robot and Meta-World benchmarks showed LaGO significantly improved success rates, increasing them from 15.1% to 27.2% on CLEVR-Robot and from 2.7% to 15.2% on Meta-World compared to Vanilla PPO. The study also indicated that more powerful pretrained LLMs yield more effective guidance, suggesting LLM knowledge can enhance planning and decision-making. AI

IMPACT Enhances reinforcement learning by integrating LLM knowledge, potentially improving planning and decision-making in AI agents.

RANK_REASON The cluster contains an academic paper detailing a new framework for reinforcement learning.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

LaGO framework uses LLMs to improve online reinforcement learning · 2 sources tracked

COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Kuan-Yen Liu, Ren-Jyun Huang, Ti-Rong Wu ·

    LaGO: Latent Action Guidance for Online Reinforcement Learning

    arXiv:2606.24669v1 Announce Type: new Abstract: Large language models (LLMs) have shown strong potential for planning and sequential decision-making, but prior work often relies on using them as direct controllers, which requires precise action generation and can be unreliable in…

  2. arXiv cs.AI TIER_1 English(EN) · Ti-Rong Wu ·

    LaGO: Latent Action Guidance for Online Reinforcement Learning

    Large language models (LLMs) have shown strong potential for planning and sequential decision-making, but prior work often relies on using them as direct controllers, which requires precise action generation and can be unreliable in practice. This paper proposes Latent Action Gui…

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