mlsanityData to 7 Oct 2026

PAIR: Prefix-Aware Internal Reward Model for Multi-Turn Agent Optimization

arXiv 2605.17877 ↗First posted 18 May 2026cs.AIcited by 5

On the map: LLM reasoning → Reinforcement learning for reasoning

also in 2 more
Abstract (arXiv)

A significant hurdle for current LLMs is the execution of complex, multi-stage tasks. Group Relative Policy Optimization (GRPO) has been emerging as a leading choice, but its reliance on sparse outcome rewards severely limits credit assignment across intermediate steps. Existing remedies such as running full rollouts to assign step-level advantages, calling external LLM judges at each step, or computing intrinsic rewards that require ground-truth answers at every evaluation introduce significant costs or practical constraints. We hypothesize that internal correctness probing over LLM hidden states can be repurposed as a step-level reward signal, potentially addressing all of these limitations at once. However, existing probing research assumes clean inputs, and we first show that this assumption breaks down in multi-step settings: hidden-state probes degrade severely under prefix contamination tracking coherence with the (possibly corrupted) prefix rather than grounded correctness, while attention-based features remain robust to contamination but underperform on clean prefixes. Building on this complementary relationship, we propose the Prefix-Aware Internal Reward (PAIR), a two-stage model with a frozen hidden-state probe estimating belief-consistency and a lightweight attention-based head correcting it toward grounded correctness. Experimental results show that PAIR achieves the highest AUROC on contaminated trajectories while operating at negligible inference cost, enabling dense step-level reward signals for GRPO training without external model calls, ground-truth dependencies, or full-trajectory rollouts.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Placed in Reinforcement learning for reasoning, one of 17 directions in LLM reasoning. Builds on 29 of its 34 references that are in the map. First posted on arXiv 18 May 2026.

Builds on

29 of 34 references.

Closest ancestors · 11

Same direction, newest first

  1. 2026Demystifying Group Relative Policy Optimization: Its Policy Gradient is a U-Statistic1 Mar
  2. 2025AgentPRM: Process Reward Models for LLM Agents via Step-Wise Promise and Progress11 Nov
  3. 2025Dynamic and Generalizable Process Reward Modeling23 Jul
  4. 2025Critique-GRPO: Advancing LLM Reasoning with Natural Language and Numerical Feedback3 Jun
  5. 2025Understanding R1-Zero-Like Training: A Critical Perspective26 Mar
  6. 2025The Lessons of Developing Process Reward Models in Mathematical Reasoning13 Jan
  7. 2024Entropy-Regularized Process Reward Model15 Dec
  8. 2023Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations14 Dec
  9. 2023Let's reward step by step: Step-Level reward model as the Navigators for Reasoning16 Oct
  10. 2023Let's Verify Step by Step31 May
  11. 2017Proximal Policy Optimization Algorithms20 Jul

Other references · 18

  1. 2022Training language models to follow instructions with human feedbackcited by 15,712
  2. 2024DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Modelscited by 8,851
  3. 2024Qwen2.5 Technical Reportcited by 4,713
  4. 2020Learning to summarize from human feedbackcited by 2,562
  5. 2023ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIscited by 1,880
  6. 2022Language Models (Mostly) Know What They Knowcited by 1,783
  7. 2024A Survey on LLM-as-a-Judgecited by 1,384
  8. 2022Discovering Latent Knowledge in Language Models Without Supervisioncited by 733
  9. 2024Lookback Lens: Detecting and Mitigating Contextual Hallucinations in Large Language Models Using Only Attention Mapscited by 140
  10. 2025VisualPRM: An Effective Process Reward Model for Multimodal Reasoningcited by 134
  11. 2025SWEET-RL: Training Multi-Turn LLM Agents on Collaborative Reasoning Taskscited by 108
  12. 2024GTA: A Benchmark for General Tool Agentscited by 77
6 more
  1. 2025Tree Search for LLM Agent Reinforcement Learningcited by 73
  2. 2025SPA-RL: Reinforcing LLM Agents via Stepwise Progress Attributioncited by 68
  3. 2024Latent Space Chain-of-Embedding Enables Output-free LLM Self-Evaluationcited by 56
  4. 2026TIPS: Turn-Level Information-Potential Reward Shaping for Search-Augmented LLMscited by 24
  5. 2025Beyond the Final Answer: Evaluating the Reasoning Trajectories of Tool-Augmented Agentscited by 20
  6. 2026AT2PO: Agentic Turn-based Policy Optimization via Tree Searchcited by 7

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 5.

5 papers.

  1. 2026Search-G1: Grounded Search Agents via Representation-Based Intrinsic Rewardscited by 0
  2. 2026It's the Problem, Not the Path: Budget and Difficulty Confounds in LLM Reasoning Trajectoriescited by 0
  3. 2026ProCredit: From Outcome Rewards to Progress Credit in Agentic Reinforcement Learningcited by 0
  4. 2026Before the Rollout Ends: Early Terminal Reward Prediction for Long-horizon Coding Agentscited by 0
  5. 2026My FAULT: Self-Diagnosis as Credit Assignment in Self-Evolving Agentic Reinforcement Learningcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Reinforcing Multi-Turn Reasoning in LLM Agents via Fine-Grained Reward Structure and Credit Assignment
  2. 2026Hidden States Know Where Reasoning Diverges: Credit Assignment via Span-Level Wasserstein Distance
  3. 2026Coverage, Not Targeting: A Structural Regime in Multi-Turn Agent Credit Assignment
  4. 2026Learning from Viable Failure Prefixes: Milestone Viability Potential Policy Optimization for Long-Horizon LLM Agents
  5. 2025GRPO is Secretly a Process Reward Model
  6. 2025Graph-Enhanced Policy Optimization in LLM Agent Training
  7. 2026Enhancing LLM-based Search Agents via Contribution Weighted Group Relative Policy Optimization
  8. 2026Unifying Temporal and Structural Credit Assignment in LLM-Based Multi-Agent Prompt Optimization