mlsanityData to 7 Oct 2026

From Reasoning to Agentic: Credit Assignment in Reinforcement Learning for Large Language Models

arXiv 2604.09459 ↗First posted 10 Apr 2026cs.CLcited by 19

On the map: LLM reasoning → Reinforcement learning for reasoning

also in 1 more
Abstract (arXiv)

Reinforcement learning (RL) for large language models (LLMs) increasingly relies on sparse outcome rewards, yet such rewards say little about which token, reasoning step, tool call, memory operation, or agent caused an outcome. This credit assignment (CA) problem spans reasoning RL and becomes sharper in agentic RL, where environment interaction introduces transition non-closure, partial observability, limited replay, heterogeneous actions, weak intermediate verifiability, and agent coupling. We synthesize a unified corpus of 69 papers published from January 2024 through July 31, 2026: 56 core CA methods and 13 adjacent or boundary enablers, selected from 92 deduplicated screening records. We retain the original granularity-by-methodology taxonomy and add a six-diagnostic framework mapping assumption breaks to identification barriers, estimators, and evaluation controls. A source-located full-text audit covers a fixed 42-core-paper subset. Two algorithm researchers independently and blindly cross-coded 252 diagnostic cells, agreeing on 223 (88.5%); per-diagnostic Cohen's kappa ranges from .543 to .909, and principal-family agreement is 42/42 (kappa=1.000). Beyond taxonomy, we establish when restored-state comparisons identify a protocol-specific causal contrast, show that text-only histories can leave even the sign of credit unidentified, and introduce a reusable CA-ID Card linking each claim to its estimand, evidence provenance, and falsification test. An atomic reporting audit describes comparator, budget parity, ablation, overhead, uncertainty, and replay coverage without constructing a cross-paper leaderboard. The companion repository hosts a living catalog and decision aids; a dated release of the frozen audit bundle is planned there separately from the minimal arXiv source.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). Placed in Reinforcement learning for reasoning, one of 17 directions in LLM reasoning. Builds on 50 of its 87 references that are in the map. First posted on arXiv 10 Apr 2026.

Builds on

50 of 87 references.

Closest ancestors · 18

Same direction, newest first

  1. 2026Counterfactual Credit Policy Optimization for Multi-Agent Collaboration23 Mar
  2. 2026InT: Self-Proposed Interventions Enable Credit Assignment in LLM Reasoning20 Jan
  3. 2026Future-KL Regularized GRPO: Process-Level Credit Assignment from f-Divergence Regularization15 Jan
  4. 2025Stabilizing Off-Policy Training for Long-Horizon LLM Agent via Turn-Level Importance Sampling and Clipping-Triggered Normalization25 Nov
  5. 2025Multi-Agent Deep Research: Training Multi-Agent Systems with M-GRPO17 Nov
  6. 2025AgentPRM: Process Reward Models for LLM Agents via Step-Wise Promise and Progress11 Nov
  7. 2025Pinpointing crucial steps: Attribution-based Credit Assignment for Verifiable Reinforcement Learning10 Oct
  8. 2025Exploiting Tree Structure for Credit Assignment in RL Training of LLMs22 Sep
  9. 2025A Survey of Reinforcement Learning for Large Reasoning Models10 Sep
  10. 2025Emergent Hierarchical Reasoning in LLMs through Reinforcement Learning3 Sep
  11. 2025CAPO: Towards Enhancing LLM Reasoning through Generative Credit Assignment4 Aug
  12. 2025Self-Guided Process Reward Optimization with Redefined Step-wise Advantage for Process Reinforcement Learning2 Jul
6 more
  1. 2025Segment Policy Optimization: Effective Segment-Level Credit Assignment in RL for Large Language Models29 May
  2. 2025Reinforcing Multi-Turn Reasoning in LLM Agents via Fine-Grained Reward Structure and Credit Assignment17 May
  3. 2025Group-in-Group Policy Optimization for LLM Agent Training16 May
  4. 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning22 Jan
  5. 2024Improve Mathematical Reasoning in Language Models by Automated Process Supervision5 Jun
  6. 2017Proximal Policy Optimization Algorithms20 Jul

Other references · 32

  1. 2024DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Modelscited by 8,851
  2. 2025RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement Learningcited by 357
  3. 2024From r to Q^*: Your Language Model is Secretly a Q-Functioncited by 242
  4. 2025The Landscape of Agentic Reinforcement Learning for LLMs: A Surveycited by 195
  5. 2025SWEET-RL: Training Multi-Turn LLM Agents on Collaborative Reasoning Taskscited by 108
  6. 2025Agent Lightning: Train ANY AI Agents with Reinforcement Learningcited by 94
  7. 2025SPA-RL: Reinforcing LLM Agents via Stepwise Progress Attributioncited by 68
  8. 2023A Survey of Temporal Credit Assignment in Deep Reinforcement Learningcited by 56
  9. 2026Hindsight Credit Assignment for Long-Horizon LLM Agentscited by 51
  10. 2025Agentic Reinforcement Learning with Implicit Step Rewardscited by 45
  11. 2025Information Gain-based Policy Optimization: A Simple and Effective Approach for Multi-Turn Search Agentscited by 43
  12. 2025CriticSearch: Fine-Grained Credit Assignment for Search Agents via a Retrospective Criticcited by 29
20 more
  1. 2025Turn-PPO: Turn-Level Advantage Estimation with PPO for Improved Multi-Turn RL in Agentic LLMscited by 28
  2. 2024From Novice to Expert: LLM Agent Policy Optimization via Step-wise Reinforcement Learningcited by 21
  3. 2026SCRIBE: Structured Mid-Level Supervision for Tool-Using Language Modelscited by 20
  4. 2025Leveraging Large Language Models for Effective and Explainable Multi-Agent Credit Assignmentcited by 17
  5. 2024Reinforcing Language Agents via Policy Optimization with Action Decompositioncited by 16
  6. 2026Dr. MAS: Stable Reinforcement Learning for Multi-Agent LLM Systemscited by 15
  7. 2024T-REG: Preference Optimization with Token-Level Reward Regularizationcited by 13
  8. 2025SCAR: Shapley Credit Assignment for More Efficient RLHFcited by 13
  9. 2025PilotRL: Training Language Model Agents via Global Planning-Guided Progressive Reinforcement Learningcited by 11
  10. 2025Process-Supervised Reinforcement Learning for Interactive Multimodal Tool-Use Agentscited by 11
  11. 2024RED: Unleashing Token-Level Rewards from Holistic Feedback via Reward Redistributioncited by 10
  12. 2025Enhancing Agentic RL with Progressive Reward Shaping and Value-based Sampling Policy Optimizationcited by 9
  13. 2026Exact Is Easier: Credit Assignment for Cooperative LLM Agentscited by 9
  14. 2026SketchVL: Policy Optimization via Fine-Grained Credit Assignment for Chart Understanding and Morecited by 7
  15. 2026Who Deserves the Reward? SHARP: Shapley Credit-based Optimization for Multi-Agent Systemcited by 7
  16. 2025QLLM: Do We Really Need a Mixing Network for Credit Assignment in Multi-Agent Reinforcement Learning?cited by 6
  17. 2025CARL: Criticality-Aware Agentic Reinforcement Learningcited by 5
  18. 2026Implicit Turn-Wise Policy Optimization for Proactive User-LLM Interactioncited by 3
  19. 2024Adaptive Segment-level Reward: Bridging the Gap Between Action and Reward Space in Alignmentcited by 2
  20. 2026Scaling Multiagent Systems with Process Rewardscited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 19.

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  1. 2026CAPO: Critic-Guided Action-Aligned Policy Optimization for Advancing LLM Agent Capabilitiescited by 8
  2. 2026CEPO: RLVR Self-Distillation using Contrastive Evidence Policy Optimizationcited by 7
  3. 2026Credit Without Ground Truth: Auditing Step-Level Credit Assignment in LLM Agents Against Executed Replaycited by 5
  4. 2026ECHO: Prune To Act, Trace To Learn With Selective Turn Memory In Agentic RLcited by 3
  5. 2026PiCA: Pivot-Based Credit Assignment for Search Agentic Reinforcement Learningcited by 2
  6. 2026Process Advantage Signal Shaping: A Paradigm-Agnostic Middleware for Process-Supervised RL in LLM Reasonerscited by 2
  7. 2026Agentic ESOpt: Fine-Tuning Long-Horizon LLM Agents with Minimal GPU Requirementscited by 2
  8. 2026Re-feeding Is Not Replaying: Measuring Replay Noise in Counterfactual Token-Credit Estimationcited by 1
  9. 2026RLVP: Penalize the Path, Reward the Outcomecited by 1
  10. 2026SkillGate: Training In-Policy Skill Selection in Long-Horizon Agentscited by 1
  11. 2026Don't Let Gains FADE: Breaking Down Policy Gradient Weights in RLcited by 0
  12. 2026AdaStop: Cost-Aware Early Stopping for DNN Test Selectioncited by 0
7 more
  1. 2026AgentOmnia: Scaling Agentic Models for Full-Scenario Applicationscited by 0
  2. 2026Let Credit Follow Computation: Architecture-Aware Credit Transport for Large Language Model Reinforcement Learningcited by 0
  3. 2026BRACE: Anchored Bellman-Residual Correction for Stale Critics in Asynchronous RLcited by 0
  4. 2026Designer-RSI: Evolving Procedural Memory from User Traffic for Agentic Graphic Designcited by 0
  5. 2026Rollout Efficiency in Reinforcement Learning for Reasoning Large Language Models: A Taxonomy and Future Directionscited by 0
  6. 2026From Self-Distillation to Self-Practice: Privileged Information for Multi-Turn Agentscited by 0
  7. 2026R2 Flow: Recursive Self-Improvement via Recursive Skill Evolutioncited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Verifiable Process Rewards for Agentic Reasoning
  2. 2026TRACE: Turn-level Reward Assignment via Credit Estimation for Long-Horizon Agents
  3. 2025Agent-R1: A Unified and Modular Framework for Agentic Reinforcement Learning
  4. 2026Targeting Pivotal Decisions for Credit Assignment in Agentic Reinforcement Learning
  5. 2025Speaking the Language of Teamwork: LLM-Guided Credit Assignment in Multi-Agent Reinforcement Learning
  6. 2024Latent Reward: LLM-Empowered Credit Assignment in Episodic Reinforcement Learning
  7. 2026Credit Assignment with Resets in Language Model Reasoning
  8. 2026Rewarding Beliefs, Not Actions: Consistency-Guided Credit Assignment for Long-Horizon Agents