TSPO: Breaking the Double Homogenization Dilemma in Multi-turn Search Policy Optimization

• URL: http://arxiv.org/abs/2601.22776v1
• Date: Fri, 30 Jan 2026 09:58:45 GMT
• Title: TSPO: Breaking the Double Homogenization Dilemma in Multi-turn Search Policy Optimization
• Authors: Shichao Ma, Zhiyuan Ma, Ming Yang, Xiaofan Li, Xing Wu, Jintao Du, Yu Cheng, Weiqiang Wang, Qiliang Liu, Zhengyang Zhou, Yang Wang,
• Abstract summary: Multi-turn tool-integrated reasoning enables Large Language Models (LLMs) to solve complex tasks through iterative information retrieval.<n>Current reinforcement learning (RL) frameworks for search-augmented reasoning rely on sparse outcome-level rewards.<n>We propose Turn-level Stage-aware Policy Optimization (TSPO) to address this problem.
• Score: 32.17940023097263
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-turn tool-integrated reasoning enables Large Language Models (LLMs) to solve complex tasks through iterative information retrieval. However, current reinforcement learning (RL) frameworks for search-augmented reasoning predominantly rely on sparse outcome-level rewards, leading to a "Double Homogenization Dilemma." This manifests as (1) Process homogenization, where the thinking, reasoning, and tooling involved in generation are ignored. (2) Intra-group homogenization, coarse-grained outcome rewards often lead to inefficiencies in intra-group advantage estimation with methods like Group Relative Policy Optimization (GRPO) during sampling. To address this, we propose Turn-level Stage-aware Policy Optimization (TSPO). TSPO introduces the First-Occurrence Latent Reward (FOLR) mechanism, allocating partial rewards to the step where the ground-truth answer first appears, thereby preserving process-level signals and increasing reward variance within groups without requiring external reward models or any annotations. Extensive experiments demonstrate that TSPO significantly outperforms state-of-the-art baselines, achieving average performance gains of 24% and 13.6% on Qwen2.5-3B and 7B models, respectively.

Related papers

• Back to Basics: Revisiting Exploration in Reinforcement Learning for LLM Reasoning via Generative Probabilities [10.235183326885794]
  Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as an indispensable paradigm for enhancing reasoning in Large Language Models (LLMs)<n>We analyze this issue from the perspective of sampling probability dynamics, identifying that the standard objective disproportionately reinforces the highest-likelihood paths.<n>We propose a novel Advantage Re-weighting Mechanism (ARM) designed to equilibrate the confidence levels across all correct responses.
  arXiv  Detail & Related papers  (2026-02-05T04:06:55Z)
• Search-R2: Enhancing Search-Integrated Reasoning via Actor-Refiner Collaboration [49.9937230730202]
  We propose Search-R2, a novel Actor-Refiner collaboration framework that enhances reasoning through targeted intervention.<n>Our approach decomposes the generation process into an Actor, which produces initial reasoning trajectories.<n>We show that Search-R2 consistently outperforms strong RAG and RL-based baselines across model scales.
  arXiv  Detail & Related papers  (2026-02-03T15:32:09Z)
• ArenaRL: Scaling RL for Open-Ended Agents via Tournament-based Relative Ranking [84.07076200941474]
  ArenaRL is a reinforcement learning paradigm that shifts from pointwise scalar scoring to intra-group relative ranking.<n>We construct an intra-group adversarial arena and devise a tournament-based ranking scheme to obtain stable advantage signals.<n>Experiments show that ArenaRL substantially outperforms standard RL baselines.
  arXiv  Detail & Related papers  (2026-01-10T08:43:07Z)
• IRPO: Scaling the Bradley-Terry Model via Reinforcement Learning [11.499402258204375]
  Intergroup Relative Preference Optimization (IRPO) is a novel RL framework that incorporates the well-established Bradley-Terry model into GRPO.<n>By generating a pointwise score for each response, IRPO enables efficient evaluation of arbitrarily many candidates during RL training.<n> Experimental results demonstrate that IRPO achieves state-of-the-art (SOTA) performance among pointwise GRMs.
  arXiv  Detail & Related papers  (2026-01-02T12:57:06Z)
• Coupled Variational Reinforcement Learning for Language Model General Reasoning [83.82392089177841]
  We propose textitbCoupled bVari bReinforcement bLearning (CoVRL) to bridge variational inference and reinforcement learning.<n>CoVRL improves performance by 12.4% over the base model and achieves an additional 2.3% improvement over strong state-of-the-art verifier-free RL baselines.
  arXiv  Detail & Related papers  (2025-12-14T07:03:51Z)
• DaGRPO: Rectifying Gradient Conflict in Reasoning via Distinctiveness-Aware Group Relative Policy Optimization [20.66452395111739]
  We propose Distinctiveness-aware Group Relative Policy Optimization (DaGRPO)<n>DaGRPO incorporates two core mechanisms: (1) Sequence-level Gradient Rectification, which utilizes fine-grained scoring to dynamically mask sample pairs with low distinctiveness; and (2) Off-policy Data Augmentation, which introduces high-quality anchors to recover training signals for challenging tasks.<n>In-depth analysis confirms that DaGRPO effectively mitigates gradient explosion and accelerates the emergence of long-chain reasoning capabilities.
  arXiv  Detail & Related papers  (2025-12-06T07:51:36Z)
• Empowering Multi-Turn Tool-Integrated Reasoning with Group Turn Policy Optimization [20.004150645050537]
  Group Turn Policy Optimization (GTPO) is a novel reinforcement learning algorithm designed for training Large Language Models (LLMs) on multi-turn Tool-Integrated Reasoning tasks.<n>GTPO introduces three key innovations: turn-level reward assignment that provides fine-grained feedback for individual turns, return-based advantage estimation, and self-supervised reward shaping.<n>Our comprehensive evaluation demonstrates that GTPO outperforms GRPO by 3.0% on average across diverse reasoning benchmarks.
  arXiv  Detail & Related papers  (2025-11-18T19:01:16Z)
• Repurposing Synthetic Data for Fine-grained Search Agent Supervision [81.95597592711688]
  LLM-based search agents are increasingly trained on entity-centric synthetic data.<n> prevailing training methods discard this rich entity information, relying instead on sparse, outcome-based rewards.<n>We introduce Entity-aware Group Relative Policy Optimization (E-GRPO), a novel framework that formulates a dense entity-aware reward function.
  arXiv  Detail & Related papers  (2025-10-28T17:50:40Z)
• G$^2$RPO: Granular GRPO for Precise Reward in Flow Models [74.21206048155669]
  We propose a novel Granular-GRPO (G$2$RPO) framework that achieves precise and comprehensive reward assessments of sampling directions.<n>We introduce a Multi-Granularity Advantage Integration module that aggregates advantages computed at multiple diffusion scales.<n>Our G$2$RPO significantly outperforms existing flow-based GRPO baselines.
  arXiv  Detail & Related papers  (2025-10-02T12:57:12Z)
• Solving the Granularity Mismatch: Hierarchical Preference Learning for Long-Horizon LLM Agents [56.625878022978945]
  Large Language Models (LLMs) as autonomous agents are increasingly tasked with solving complex, long-horizon problems.<n>Direct Preference Optimization (DPO) provides a signal that is too coarse for precise credit assignment, while step-level DPO is often too myopic to capture the value of multi-step behaviors.<n>We introduce Hierarchical Preference Learning (HPL), a hierarchical framework that optimize LLM agents by leveraging preference signals at multiple, synergistic granularities.
  arXiv  Detail & Related papers  (2025-09-26T08:43:39Z)
• MMR1: Enhancing Multimodal Reasoning with Variance-Aware Sampling and Open Resources [113.33902847941941]
  Variance-Aware Sampling (VAS) is a data selection strategy guided by Variance Promotion Score (VPS)<n>We release large-scale, carefully curated resources containing 1.6M long CoT cold-start data and 15k RL QA pairs.<n> Experiments across mathematical reasoning benchmarks demonstrate the effectiveness of both the curated data and the proposed VAS.
  arXiv  Detail & Related papers  (2025-09-25T14:58:29Z)
• Group Causal Policy Optimization for Post-Training Large Language Models [10.791474908144703]
  Group Relative Policy Optimization ( GRPO) treats candidate responses as independent, overlooking semantic interactions such as complementarity and contradiction.<n>We propose Group Causal Policy Optimization (GCPO), which integrates causal structure into optimization through two key components.<n>GCPO consistently surpasses existing methods, including GRPO across multiple reasoning benchmarks.
  arXiv  Detail & Related papers  (2025-08-07T14:17:28Z)
• EDGE-GRPO: Entropy-Driven GRPO with Guided Error Correction for Advantage Diversity [7.818698554631196]
  Group Relative Policy Optimization (GRPO) algorithm relies on sparse reward rules, leading to the advantage collapse problem.<n>We propose the EDGE-GRPO algorithm, which adopts textbfEntropy-textbfDriven Advantage and textbfGuided textbfError Correction to effectively mitigate the problem of advantage collapse.
  arXiv  Detail & Related papers  (2025-07-29T14:23:58Z)

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.