Foresight Optimization for Strategic Reasoning in Large Language Models

• URL: http://arxiv.org/abs/2604.13592v2
• Date: Thu, 16 Apr 2026 06:54:57 GMT
• Title: Foresight Optimization for Strategic Reasoning in Large Language Models
• Abstract summary: We introduce Foresight Policy Optimization (FoPO) to enhance strategic reasoning in large language models (LLMs)<n>FoPO integrates opponent modeling principles into policy optimization, thereby enabling explicit consideration of both self-interest and counterpart influence.<n>Experiments demonstrate that FoPO significantly enhances strategic reasoning across LLMs of varying sizes and origins.
• Score: 24.13855510359357
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reasoning capabilities in large language models (LLMs) have generally advanced significantly. However, it is still challenging for existing reasoning-based LLMs to perform effective decision-making abilities in multi-agent environments, due to the absence of explicit foresight modeling. To this end, strategic reasoning, the most fundamental capability to anticipate the counterpart's behaviors and foresee its possible future actions, has been introduced to alleviate the above issues. Strategic reasoning is fundamental to effective decision-making in multi-agent environments, yet existing reasoning enhancement methods for LLMs do not explicitly capture its foresight nature. In this work, we introduce Foresight Policy Optimization (FoPO) to enhance strategic reasoning in LLMs, which integrates opponent modeling principles into policy optimization, thereby enabling explicit consideration of both self-interest and counterpart influence. Specifically, we construct two curated datasets, namely Cooperative RSA and Competitive Taboo, equipped with well-designed rules and moderate difficulty to facilitate a systematic investigation of FoPO in a self-play framework. Our experiments demonstrate that FoPO significantly enhances strategic reasoning across LLMs of varying sizes and origins. Moreover, models trained with FoPO exhibit strong generalization to out-of-domain strategic scenarios, substantially outperforming standard LLM reasoning optimization baselines.

Related papers

• Agentic AI Reasoning for Mobile Edge General Intelligence: Fundamentals, Approaches, and Directions [74.35421055079655]
  Large language models (LLMs) have enabled an emergence of agentic artificial intelligence (AI) with powerful reasoning and autonomous decision-making capabilities.<n>Mobile Edge General Intelligence (MEGI) brings real-time, privacy-preserving reasoning to the network edge.<n>We propose a joint optimization framework for efficient LLM reasoning deployment in MEGI.
  arXiv  Detail & Related papers  (2025-09-27T10:53:48Z)
• GPO: Learning from Critical Steps to Improve LLM Reasoning [13.271737599933147]
  We introduce textbfGuided textbfPivotal textbfOptimization (GPO), a novel fine-tuning strategy that dives into the reasoning process to enable more effective improvements.<n>We demonstrate that GPO is a general strategy that can be integrated with various optimization methods to improve reasoning performance.
  arXiv  Detail & Related papers  (2025-09-19T22:30:23Z)
• Feedback-Induced Performance Decline in LLM-Based Decision-Making [6.5990946334144756]
  Large Language Models (LLMs) can extract context from natural language problem descriptions.<n>This paper studies the behaviour of these models within a Markov Decision Process (MDPs)
  arXiv  Detail & Related papers  (2025-07-20T10:38:56Z)
• EPO: Explicit Policy Optimization for Strategic Reasoning in LLMs via Reinforcement Learning [69.55982246413046]
  We propose explicit policy optimization (EPO) for strategic reasoning.<n>We train the strategic reasoning model via multi-turn reinforcement learning (RL),utilizing process rewards and iterative self-play.<n>Our findings reveal various collaborative reasoning mechanisms emergent in EPO and its effectiveness in generating novel strategies.
  arXiv  Detail & Related papers  (2025-02-18T03:15:55Z)
• Teaching LLMs According to Their Aptitude: Adaptive Reasoning for Mathematical Problem Solving [55.895917967408586]
  Existing approaches to mathematical reasoning with large language models rely on Chain-of-Thought (CoT) for generalizability or Tool-Integrated Reasoning (TIR) for precise computation.<n>We propose TATA (Teaching LLMs According to Their Aptitude), an adaptive framework that enables LLMs to personalize their reasoning strategy spontaneously.
  arXiv  Detail & Related papers  (2025-02-17T16:56:23Z)
• Meta Reasoning for Large Language Models [58.87183757029041]
  We introduce Meta-Reasoning Prompting (MRP), a novel and efficient system prompting method for large language models (LLMs) MRP guides LLMs to dynamically select and apply different reasoning methods based on the specific requirements of each task. We evaluate the effectiveness of MRP through comprehensive benchmarks.
  arXiv  Detail & Related papers  (2024-06-17T16:14:11Z)
• STRIDE: A Tool-Assisted LLM Agent Framework for Strategic and Interactive Decision-Making [43.734386326024016]
  Large Language Models (LLMs) have revolutionized natural language processing, showing remarkable linguistic proficiency and reasoning capabilities. This paper presents a novel framework equipped with memory and specialized tools to enhance their strategic decision-making capabilities.
  arXiv  Detail & Related papers  (2024-05-25T23:25:10Z)
• LLM as a Mastermind: A Survey of Strategic Reasoning with Large Language Models [75.89014602596673]
  Strategic reasoning requires understanding and predicting adversary actions in multi-agent settings while adjusting strategies accordingly. We explore the scopes, applications, methodologies, and evaluation metrics related to strategic reasoning with Large Language Models. It underscores the importance of strategic reasoning as a critical cognitive capability and offers insights into future research directions and potential improvements.
  arXiv  Detail & Related papers  (2024-04-01T16:50:54Z)
• K-Level Reasoning: Establishing Higher Order Beliefs in Large Language Models for Strategic Reasoning [76.3114831562989]
  It requires Large Language Model (LLM) agents to adapt their strategies dynamically in multi-agent environments. We propose a novel framework: "K-Level Reasoning with Large Language Models (K-R)"
  arXiv  Detail & Related papers  (2024-02-02T16:07:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.