Thought-Augmented Policy Optimization: Bridging External Guidance and Internal Capabilities

• URL: http://arxiv.org/abs/2505.15692v2
• Date: Mon, 26 May 2025 15:56:19 GMT
• Title: Thought-Augmented Policy Optimization: Bridging External Guidance and Internal Capabilities
• Authors: Jinyang Wu, Chonghua Liao, Mingkuan Feng, Shuai Zhang, Zhengqi Wen, Pengpeng Shao, Huazhe Xu, Jianhua Tao,
• Abstract summary: Reinforcement learning (RL) has emerged as an effective method for training reasoning models.<n>We propose TAPO, a framework that augments RL by incorporating external high-level guidance ("thought patterns")<n>Our approach significantly outperforms GRPO by 99% on AIME, 41% on AMC, and 17% on Minerva Math.
• Score: 45.989423626537985
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reinforcement learning (RL) has emerged as an effective method for training reasoning models. However, existing RL approaches typically bias the model's output distribution toward reward-maximizing paths without introducing external knowledge. This limits their exploration capacity and results in a narrower reasoning capability boundary compared to base models. To address this limitation, we propose TAPO (Thought-Augmented Policy Optimization), a novel framework that augments RL by incorporating external high-level guidance ("thought patterns"). By adaptively integrating structured thoughts during training, TAPO effectively balances model-internal exploration and external guidance exploitation. Extensive experiments show that our approach significantly outperforms GRPO by 99% on AIME, 41% on AMC, and 17% on Minerva Math. Notably, these high-level thought patterns, abstracted from only 500 prior samples, generalize effectively across various tasks and models. This highlights TAPO's potential for broader applications across multiple tasks and domains. Our further analysis reveals that introducing external guidance produces powerful reasoning models with superior explainability of inference behavior and enhanced output readability.

Related papers

• Perception-Aware Policy Optimization for Multimodal Reasoning [79.56070395437898]
  A major source of error in current multimodal reasoning lies in the perception of visual inputs.<n>We propose PAPO, a novel policy gradient algorithm that encourages the model to learn to perceive while learning to reason.<n>We observe a substantial reduction of 30.5% in perception errors, indicating improved perceptual capabilities with PAPO.
  arXiv  Detail & Related papers  (2025-07-08T23:22:34Z)
• ExPO: Unlocking Hard Reasoning with Self-Explanation-Guided Reinforcement Learning [12.83211408922535]
  Reinforcement learning-style post-training improves reasoning by optimizing model outputs based on reward or preference signals.<n> GRPO-style approaches implement this by using self-generated samples labeled by an outcome-based verifier.<n>We propose $textbfSelf-Explanation Policy Optimization (ExPO)$-a simple and modular framework that generates such samples by conditioning on the ground-truth answer.
  arXiv  Detail & Related papers  (2025-07-03T17:44:55Z)
• Vision-EKIPL: External Knowledge-Infused Policy Learning for Visual Reasoning [17.421901873720156]
  This paper proposes a novel RL framework called textbfVision-EKIPL.<n>It introduces high-quality actions generated by external auxiliary models during the RL training process to guide the optimization of the policy model.<n>It achieves up to a 5% performance improvement on the Reason-RFT-CoT Benchmark compared to the state-of-the-art (SOTA)
  arXiv  Detail & Related papers  (2025-06-07T16:37:46Z)
• Beyond Accuracy: Dissecting Mathematical Reasoning for LLMs Under Reinforcement Learning [82.43575191712726]
  We introduce a fine-grained analytic framework to dissect the impact ofReinforcement learning on reasoning.<n>Our framework specifically investigates key elements that have been hypothesized to benefit from RL training.
  arXiv  Detail & Related papers  (2025-06-05T07:53:59Z)
• Training Large Language Models to Reason via EM Policy Gradient [0.27195102129094995]
  We introduce an off-policy reinforcement learning algorithm, EM Policy Gradient, to enhance LLM reasoning.<n>We evaluate the effectiveness of EM Policy Gradient on the GSM8K and MATH (HARD) datasets.<n>Models fine-tuned with our method exhibit cognitive behaviors, such as sub-problem decomposition, self-verification, and backtracking.
  arXiv  Detail & Related papers  (2025-04-24T01:31:05Z)
• Effectively Controlling Reasoning Models through Thinking Intervention [41.38412282063417]
  Reasoning-enhanced large language models (LLMs) explicitly generate intermediate reasoning steps prior to generating final answers.<n>We propose Thinking Intervention, a novel paradigm designed to explicitly guide the internal reasoning processes of LLMs.
  arXiv  Detail & Related papers  (2025-03-31T17:50:13Z)
• Enhancing LLM Reasoning with Iterative DPO: A Comprehensive Empirical Investigation [29.579349371114702]
  Direct Preference Optimization (DPO) is a cost-effective alternative to reinforcement learning (RL) for large language models (LLMs)<n>We show that a single round of DPO with coarse filtering significantly enhances mathematical reasoning performance.<n>With simple verifiable rewards, our model achieves RL-level performance with significantly lower computational overhead.
  arXiv  Detail & Related papers  (2025-03-17T06:28:25Z)
• A Survey of Direct Preference Optimization [103.59317151002693]
  Large Language Models (LLMs) have demonstrated unprecedented generative capabilities.<n>Their alignment with human values remains critical for ensuring helpful and harmless deployments.<n>Direct Preference Optimization (DPO) has recently gained prominence as a streamlined alternative.
  arXiv  Detail & Related papers  (2025-03-12T08:45:15Z)
• On the Modeling Capabilities of Large Language Models for Sequential Decision Making [52.128546842746246]
  Large pretrained models are showing increasingly better performance in reasoning and planning tasks. We evaluate their ability to produce decision-making policies, either directly, by generating actions, or indirectly. In environments with unfamiliar dynamics, we explore how fine-tuning LLMs with synthetic data can significantly improve their reward modeling capabilities.
  arXiv  Detail & Related papers  (2024-10-08T03:12:57Z)
• Self-Training with Direct Preference Optimization Improves Chain-of-Thought Reasoning [5.487210426671288]
  In this work, we demonstrate that the reasoning abilities of small-scale LMs can be enhanced through self-training. We also show that the conventional self-training can be further augmented by a preference learning algorithm called Direct Preference Optimization.
  arXiv  Detail & Related papers  (2024-07-25T17:59:16Z)
• Exploratory Preference Optimization: Harnessing Implicit Q*-Approximation for Sample-Efficient RLHF [82.7679132059169]
  Reinforcement learning from human feedback has emerged as a central tool for language model alignment. We propose a new algorithm for online exploration in RLHF, Exploratory Preference Optimization (XPO) XPO enjoys the strongest known provable guarantees and promising empirical performance.
  arXiv  Detail & Related papers  (2024-05-31T17:39:06Z)
• Model Extrapolation Expedites Alignment [135.12769233630362]
  We propose a method called ExPO to expedite alignment training with human preferences.<n>We demonstrate that ExPO boosts a DPO model trained with only 20% steps to outperform the fully-trained one.<n>We show that ExPO notably improves existing open-source LLMs on the leading AlpacaEval 2.0 and MT-Bench benchmarks.
  arXiv  Detail & Related papers  (2024-04-25T17:39:50Z)
• When Parameter-efficient Tuning Meets General-purpose Vision-language Models [65.19127815275307]
  PETAL revolutionizes the training process by requiring only 0.5% of the total parameters, achieved through a unique mode approximation technique. Our experiments reveal that PETAL not only outperforms current state-of-the-art methods in most scenarios but also surpasses full fine-tuning models in effectiveness.
  arXiv  Detail & Related papers  (2023-12-16T17:13:08Z)

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.