mlsanityData to 14 Oct 2026

Exploiting Tree Structure for Credit Assignment in RL Training of LLMs

arXiv 2509.18314 ↗First posted 22 Sep 2025cs.CLcited by 27

On the map: LLM reasoning → Reinforcement learning for reasoning

Abstract (arXiv)

Reinforcement learning improves LLM reasoning, yet sparse delayed reward over long sequences makes token-level credit assignment the key bottleneck. We study the verifiable-reward setting, where the final answer is checkable and multiple responses can be drawn per prompt. Reasoning tasks in math and medical QA align with this setup, where only a few decision tokens significantly impact the outcome. PPO offers token-level advantages with a learned value model, but it is complex to train both the actor and critic models simultaneously, and it is not easily generalizable, as the token-level values from the critic model can make training prone to overfitting. GRPO is critic-free and supports verifiable rewards, but spreads a single sequence-level return across tokens and ignores branching. We introduce Prefix-to-Tree (P2T), a simple procedure that converts a group of responses into a prefix tree and computes nonparametric prefix values (V(s)) by aggregating descendant outcomes. Built on P2T, we propose TEMPO (emphTree-Estimated Mean Prefix Value for Policy Optimization), a critic-free algorithm that augments the group-relative outcome signal of GRPO with branch-gated temporal-difference corrections derived from the tree. At non-branch tokens, the temporal-difference (TD) term is zero, so TEMPO reduces to GRPO; at branching tokens, it supplies precise token-level credit without a learned value network or extra judges/teachers. On Qwen3-1.7B/4B, TEMPO outperforms PPO and GRPO on in-distribution (MATH, MedQA) and out-of-distribution (GSM-HARD, AMC23, MedMCQA, MMLU-Medical) benchmarks, and reaches higher validation accuracy with roughly the same wall-clock time.

Key facts. Cited by 27 papers in the map (data through 14 Oct 2026). Placed in Reinforcement learning for reasoning, one of 17 directions in LLM reasoning. Builds on 32 of its 39 references that are in the map. First posted on arXiv 22 Sep 2025.

Builds on

32 of 39 references.

Closest ancestors · 15

Same direction, newest first

  1. 2025Tree-OPO: Off-policy Monte Carlo Tree-Guided Advantage Optimization for Multistep Reasoning11 Sep
  2. 2025TreePO: Bridging the Gap of Policy Optimization and Efficacy and Inference Efficiency with Heuristic Tree-based Modeling24 Aug
  3. 2025TreeRPO: Tree Relative Policy Optimization5 Jun
  4. 2025Beyond the 80/20 Rule: High-Entropy Minority Tokens Drive Effective Reinforcement Learning for LLM Reasoning2 Jun
  5. 2025DAPO: An Open-Source LLM Reinforcement Learning System at Scale18 Mar
  6. 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning22 Jan
  7. 2024Rewarding Progress: Scaling Automated Process Verifiers for LLM Reasoning10 Oct
  8. 2024Selective Preference Optimization via Token-Level Reward Function Estimation24 Aug
  9. 2024TLCR: Token-Level Continuous Reward for Fine-grained Reinforcement Learning from Human Feedback23 Jul
  10. 2024Unpacking DPO and PPO: Disentangling Best Practices for Learning from Preference Feedback13 Jun
  11. 2024ReST-MCTS*: LLM Self-Training via Process Reward Guided Tree Search6 Jun
  12. 2024AlphaMath Almost Zero: Process Supervision without Process6 May
3 more
  1. 2023Let's Verify Step by Step31 May
  2. 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Model29 May
  3. 2017Proximal Policy Optimization Algorithms20 Jul

Other references · 17

  1. 2022Training language models to follow instructions with human feedbackcited by 15,971
  2. 2024DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Modelscited by 9,379
  3. 2025Qwen3 Technical Reportcited by 8,820
  4. 2023Efficient Memory Management for Large Language Model Serving with PagedAttentioncited by 5,944
  5. 2021Measuring Mathematical Problem Solving With the MATH Datasetcited by 5,371
  6. 2015High-Dimensional Continuous Control Using Generalized Advantage Estimationcited by 2,026
  7. 2022Large Language Models Encode Clinical Knowledgecited by 1,830
  8. 2020What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Examscited by 1,356
  9. 2022PAL: Program-aided Language Modelscited by 652
  10. 2022MedMCQA : A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answeringcited by 543
  11. 2023Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Modelscited by 349
  12. 2024RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Foldcited by 99
5 more
  1. 2025TreeRL: LLM Reinforcement Learning with On-Policy Tree Searchcited by 80
  2. 2024Dense Reward for Free in Reinforcement Learning from Human Feedbackcited by 78
  3. 2024Step-level Value Preference Optimization for Mathematical Reasoningcited by 72
  4. 2024The N+ Implementation Details of RLHF with PPO: A Case Study on TL;DR Summarizationcited by 50
  5. 2024Self-Explore: Enhancing Mathematical Reasoning in Language Models with Fine-grained Rewardscited by 31

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 25.

27 papers.

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By area:

  1. 2026From Reasoning to Agentic: Credit Assignment in Reinforcement Learning for Large Language Modelscited by 23
  2. 2026RTMC: Step-Level Credit Assignment via Rollout Treescited by 12
  3. 2026MatchTIR: Fine-Grained Supervision for Tool-Integrated Reasoning via Bipartite Matchingcited by 10
  4. 2026Generate, Filter, Control, Replay: A Comprehensive Survey of Rollout Strategies for LLM Reinforcement Learningcited by 9
  5. 2026Where Hindsight Credit Can Reside: A Signed-Capacity View of Token Updates in RLVRcited by 8
  6. 2025CARL: Criticality-Aware Agentic Reinforcement Learningcited by 5
  7. 2026TreePS-RAG: Tree-based Process Supervision for Reinforcement Learning in Agentic RAGcited by 5
  8. 2026Execution-Grounded Credit Assignment for GRPO in Code Generationcited by 5
  9. 2025TaoSR-AGRL: Adaptive Guided Reinforcement Learning Framework for E-commerce Search Relevancecited by 4
  10. 2026TopoCurate:Modeling Interaction Topology for Tool-Use Agent Trainingcited by 4
  11. 2026Self-Induced Outcome Potential: Turn-Level Credit Assignment for Agents without Verifierscited by 4
  12. 2026IAPO: Information-Aware Policy Optimization for Token-Efficient Reasoningcited by 3
15 more
  1. 2026MICA: Multi-granularity Intertemporal Credit Assignment for Long-Horizon Emotional Support Dialoguecited by 3
  2. 2026Structured Role-Aware Policy Optimization for Multimodal Reasoningcited by 2
  3. 2026Modularized Reinforcement Learning on LLMs: From MDP Creation to Exploration and Learningcited by 2
  4. 2026Difficulty-Adaptive Tree-Structured Policy Optimization for Expanding Reasoning Coverage in RLVRcited by 2
  5. 2026Rethinking Critic Learning in PPO: Understanding and Mitigating Value Flatteningcited by 2
  6. 2026Teaching the Way, Not the Answer: Privileged Tutoring Distillation for Multimodal Policy Optimizationcited by 1
  7. 2026When Implausible Tokens Get Reinforced: Tail-Aware Credit Calibration for LLM Reinforcement Learningcited by 1
  8. 2026Graph-Based Chain-of-Thought Pruning for Reducing Redundant Reflections in Reasoning LLMscited by 0
  9. 2026Reinforcing 3D Understanding in Point-VLMs via Geometric Reward Credit Assignmentcited by 0
  10. 2026ARCA: Adapter-Residual Credit Assignment When Token Signals Degeneratecited by 0
  11. 2026LEAF: Growing Trees Without Branching for Speech-Aware Large Language Model Post-Trainingcited by 0
  12. 2026Learning with a Single Rollout via Pass@k Policy Optimizationcited by 0
  13. 2026RP-OPSD: Reasoning-Pivot-Guided On-Policy Self-Distillation for Multilingual Reasoning Transfercited by 0
  14. 2026Let Credit Follow Computation: Architecture-Aware Credit Transport for Large Language Model Reinforcement Learningcited by 0
  15. 2026Advancing Entropy-Level Credit Assignment in RLVR via Proximal Entropy Policy Optimizationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025GRPO-λ: Credit Assignment improves LLM Reasoning
  2. 2026OPPO: Bayesian Value Recursion for Token-Level Credit Assignment in LLM Reasoning
  3. 2026InT: Self-Proposed Interventions Enable Credit Assignment in LLM Reasoning
  4. 2025Token-Efficient RL for LLM Reasoning
  5. 2026From Reasoning Chains to Verifiable Subproblems: Curriculum Reinforcement Learning Enables Credit Assignment for LLM Reasoning
  6. 2026Grad2Reward: From Sparse Judgment to Dense Rewards for Improving Open-Ended LLM Reasoning
  7. 2024VinePPO: Refining Credit Assignment in RL Training of LLMs
  8. 2026Learning from Own Solutions: Self-Conditioned Credit Assignment for Reinforcement Learning with Verifiable Rewards