Reinforced Language Models for Sequential Decision Making

• URL: http://arxiv.org/abs/2508.10839v1
• Date: Thu, 14 Aug 2025 17:05:44 GMT
• Title: Reinforced Language Models for Sequential Decision Making
• Authors: Jim Dilkes, Vahid Yazdanpanah, Sebastian Stein,
• Abstract summary: Large Language Models (LLMs) show potential as sequential decision-making agents.<n>Existing post-training methods are designed for single-turn interactions and cannot handle credit assignment in multi-step agentic tasks.<n>This work demonstrates that targeted post-training is a practical and efficient alternative to relying on model scale for creating sequential decision-making agents.
• Score: 6.971286730860635
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) show potential as sequential decision-making agents, but their application is often limited due to a reliance on large, computationally expensive models. This creates a need to improve smaller models, yet existing post-training methods are designed for single-turn interactions and cannot handle credit assignment in multi-step agentic tasks. To address this, we introduce Multi-Step Group-Relative Policy Optimization (MS-GRPO), a new algorithm for post-training LLM agents, grounded in formal Text-Mediated Stochastic Game (TSMG) and Language-Agent Policy (LAP) frameworks. For credit assignment, MS-GRPO attributes the entire cumulative episode reward to each individual episode step. We supplement this algorithm with a novel absolute-advantage-weighted episode sampling strategy that we show improves training performance. We evaluate our approach by post-training a 3-billion parameter model on Snake and Frozen Lake. Our experiments demonstrate that the method is effective in improving decision-making performance: our post-trained 3B parameter model outperforms a 72B parameter baseline by 50% on the Frozen Lake task. This work demonstrates that targeted post-training is a practical and efficient alternative to relying on model scale for creating sequential decision-making agents using LLMs.

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