mlsanityData to 7 Oct 2026

TIPS: Turn-Level Information-Potential Reward Shaping for Search-Augmented LLMs

arXiv 2603.22293 ↗First posted 11 Mar 2026cs.CL cs.AI cs.LGcited by 24

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Abstract (arXiv)

Search-augmented large language models (LLMs) trained with reinforcement learning (RL) have achieved strong results on open-domain question answering (QA), but training still remains a significant challenge. The optimization is often unstable due to sparse rewards and difficult credit assignments across reasoning and tool calls. To address this, we introduce Turn-Level Information Potential Reward Shaping (TIPS), a simple framework that assigns dense, turn-level rewards to each reasoning + tool-call segment based on the increased likelihood of the correct answer under a teacher model. By leveraging the potential-based reward shaping, TIPS offers fine-grained and policy-invariant guidance that overcomes the limitations of outcome-only optimization. Evaluated on seven QA benchmarks, TIPS consistently outperforms GRPO/PPO baselines and substantially improves training stability. For instance, with a Qwen-2.5 7B Instruct model, TIPS improves the average Exact Match score by 11.8% and F1 by 13.6% relative to PPO. Our results demonstrate that turn-level information-potential reward shaping provides an effective and general solution to sparse-reward credit assignment for multi-turn LLM reasoning.

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). Builds on 29 of its 40 references that are in the map. First posted on arXiv 11 Mar 2026.

Builds on

29 of 40 references.

References · 29

  1. 2022Training language models to follow instructions with human feedbackcited by 15,712
  2. 2017Proximal Policy Optimization Algorithmscited by 14,441
  3. 2024DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Modelscited by 8,851
  4. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,393
  5. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  6. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,072
  7. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,686
  8. 2023Let's Verify Step by Stepcited by 3,598
  9. 2017TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehensioncited by 2,895
  10. 2023G-Eval: NLG Evaluation using GPT-4 with Better Human Alignmentcited by 2,205
  11. 2024HybridFlow: A Flexible and Efficient RLHF Frameworkcited by 1,980
  12. 2021WebGPT: Browser-assisted question-answering with human feedbackcited by 1,722
17 more
  1. 2020Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Stepscited by 1,259
  2. 2022Text Embeddings by Weakly-Supervised Contrastive Pre-trainingcited by 1,216
  3. 2022When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memoriescited by 1,103
  4. 2022Measuring and Narrowing the Compositionality Gap in Language Modelscited by 1,054
  5. 2023Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotationscited by 895
  6. 2021MuSiQue: Multihop Questions via Single-hop Question Compositioncited by 893
  7. 2017Counterfactual Multi-Agent Policy Gradientscited by 733
  8. 2022PAL: Program-aided Language Modelscited by 635
  9. 2025BrowseComp: A Simple Yet Challenging Benchmark for Browsing Agentscited by 616
  10. 2023RLAIF vs. RLHF: Scaling Reinforcement Learning from Human Feedback with AI Feedbackcited by 615
  11. 2024Generative Verifiers: Reward Modeling as Next-Token Predictioncited by 465
  12. 2025BrowseComp-Plus: A More Fair and Transparent Evaluation Benchmark of Deep-Research Agentcited by 183
  13. 2018RUDDER: Return Decomposition for Delayed Rewardscited by 162
  14. 2025Crossing the Reward Bridge: Expanding RL with Verifiable Rewards Across Diverse Domainscited by 150
  15. 2025An Empirical Study on Reinforcement Learning for Reasoning-Search Interleaved LLM Agentscited by 43
  16. 2025Learning to Reason for Long-Form Story Generationcited by 34
  17. 2025R-Search: Empowering LLM Reasoning with Search via Multi-Reward Reinforcement Learningcited by 32

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Citing papers per year: 2026: 24.

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  1. 2026A Subgoal-driven Framework for Improving Long-Horizon LLM Agentscited by 10
  2. 2026GEAR: Granularity-Adaptive Advantage Reweighting for LLM Agents via Self-Distillationcited by 10
  3. 2026OASES: Outcome-Aligned Search-Evaluation Co-Training for Agentic Searchcited by 7
  4. 2026PAIR: Prefix-Aware Internal Reward Model for Multi-Turn Agent Optimizationcited by 5
  5. 2026Training AI Scientists to Replicate Researchcited by 4
  6. 2026Self-Induced Outcome Potential: Turn-Level Credit Assignment for Agents without Verifierscited by 3
  7. 2026Superintelligent Retrieval Agent: The Next Frontier of Agentic Retrievalcited by 2
  8. 2026PiCA: Pivot-Based Credit Assignment for Search Agentic Reinforcement Learningcited by 2
  9. 2026ARBOR: Online Process Rewards via a Reusable Rubric Buffer for Search Agentscited by 2
  10. 2026Divide and Cooperate: Role-Decomposed Multi-Agent LLM Training with Cross-Agent Learning Signalscited by 2
  11. 2026Scaling Scientific Discovery Environments for Turn-Level Agentic RLcited by 2
  12. 2026Towards Better Agents for Multi-Turn User Interaction: The Next User Turn Is More Than Contextcited by 1
12 more
  1. 2026IAPO: Influence-Aware Policy Optimization for Credit Assignment in Multi-Turn Service Agentscited by 1
  2. 2026CAFE: Self-Improving Search Agents Need Co-Evolving Feedbackcited by 1
  3. 2026CAPF: Guiding Search-Agent Rollouts with Credit-Attenuated Privileged Feedbackcited by 0
  4. 2026EvolveNav: Proactive Preflection and Self-Evolving Memory for Zero-Shot Object Goal Navigationcited by 0
  5. 2026QVal: Cheaply Evaluating Dense Supervision Signals for Long-Horizon LLM Agentscited by 0
  6. 2026PGPO: Potential-Guided Policy Optimization for Multi-Turn Agentic Taskscited by 0
  7. 2026Toolcompass: Guiding Tool Trialing, Not Suppressing Itcited by 0
  8. 2026PCQC: Privileged Counterfactual Question Credit for Multi-Turn Medical Dialoguecited by 0
  9. 2026Reinforcement Learning with Verifiable Rewards for Small Search Agentscited by 0
  10. 2026Dr.Credit: Rubric-Grounded Process Credit Assignment for Deep Research Agentscited by 0
  11. 2026SIPO: Selective-Inference Policy Optimization for Tree-Structured Agentic RLcited by 0
  12. 2026MLToolBench: Learning Tool-Augmented Agents for Machine Learning Developmentcited by 0

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Close in wording, no citation either way

  1. 2026IG-Search: Step-Level Information Gain Rewards for Search-Augmented Reasoning
  2. 2025Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning
  3. 2025Lessons from Training Grounded LLMs with Verifiable Rewards
  4. 2025Reinforced Informativeness Optimization for Long-Form Retrieval-Augmented Generation
  5. 2026Likelihood-Based Reward Designs for General LLM Reasoning
  6. 2025FrugalRAG: Less is More in RL Finetuning for Multi-Hop Question Answering
  7. 2025Learning to Think: Information-Theoretic Reinforcement Fine-Tuning for LLMs
  8. 2025StepSearch: Igniting LLMs Search Ability via Step-Wise Proximal Policy Optimization