mlsanityData to 14 Oct 2026

Teach the Magnitude, Not the Direction: Verifier-Bounded Credit Assignment for Multi-Turn Multi-step LLM Agents

arXiv 2608.13179 ↗First posted 13 Aug 2026cs.AIcited by 4

On the map: LLM agents and tool use → Reinforcement learning for LLM agents

Abstract (arXiv)

Reinforcement learning with verifiable rewards (RLVR) offers a verifier-bounded performance ceiling for training multi-turn tool-use agents, yet its trajectory-level credit assignment conflates heterogeneous per-turn outcomes into a single reward signal. On-policy distillation provides dense per-token supervision but is either teacher-bounded or prone to gradient concentration collapse. We introduce CrEST, a hierarchical credit assignment framework that retains RL's verifier-bounded ceiling while incorporating dense token-level signals from a privileged self-teacher. CrEST resolves credit at two levels: turn-segmented verified advantages address inter-turn dilution, while entropy-gated self-teacher modulation refines intra-turn token contributions. Experiments on BFCL V3 and WildToolBench show that CrEST consistently outperforms both RL and distillation baselines across two model scales, with the largest gains on long-trajectory and strict session-level metrics. Our work demonstrates that the teacher's role in policy optimization can be reduced from determining update directions to modulating update magnitudes, unlocking dense credit assignment without sacrificing the verifier-bounded ceiling.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Placed in Reinforcement learning for LLM agents, one of 16 directions in LLM agents and tool use. Builds on 34 of its 47 references that are in the map. First posted on arXiv 13 Aug 2026.

Builds on

34 of 47 references.

Closest ancestors · 7

Same direction, newest first

  1. 2026Self-Distilled Agentic Reinforcement Learning14 May
  2. 2025Don't Just Fine-tune the Agent, Tune the Environment11 Oct
  3. 2025Reinforcing Multi-Turn Reasoning in LLM Agents via Fine-Grained Reward Structure and Credit Assignment17 May
  4. 2025RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement Learning24 Apr
  5. 2025ToolRL: Reward is All Tool Learning Needs16 Apr
  6. 2025ReTool: Reinforcement Learning for Strategic Tool Use in LLMs15 Apr
  7. 2025ToRL: Scaling Tool-Integrated RL30 Mar

Other references · 27

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2024DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Modelscited by 9,379
  3. 2025Qwen3 Technical Reportcited by 8,820
  4. 2024GPT-4o System Cardcited by 4,588
  5. 2025Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilitiescited by 3,796
  6. 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,373
  7. 2025DAPO: An Open-Source LLM Reinforcement Learning System at Scalecited by 2,810
  8. 2025Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learningcited by 1,444
  9. 2025Understanding R1-Zero-Like Training: A Critical Perspectivecited by 1,345
  10. 2020ALFWorld: Aligning Text and Embodied Environments for Interactive Learningcited by 1,205
  11. 2024OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environmentscited by 1,195
  12. 2025BrowseComp: A Simple Yet Challenging Benchmark for Browsing Agentscited by 647
15 more
  1. 2023API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMscited by 591
  2. 2026Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfacescited by 585
  3. 2026Self-Distilled Reasoner: On-Policy Self-Distillation for Large Language Modelscited by 455
  4. 2025LLMs Get Lost In Multi-Turn Conversationcited by 429
  5. 2026Reinforcement Learning via Self-Distillationcited by 332
  6. 2026Rethinking On-Policy Distillation of Large Language Models: Phenomenology, Mechanism, and Recipecited by 299
  7. 2026Self-Distillation Enables Continual Learningcited by 247
  8. 2026A Survey of On-Policy Distillation for Large Language Modelscited by 163
  9. 2026Revisiting On-Policy Distillation: Empirical Failure Modes and Simple Fixescited by 145
  10. 2026Self-Distilled RLVRcited by 131
  11. 2026Why Does Self-Distillation (Sometimes) Degrade the Reasoning Capability of LLMs?cited by 103
  12. 2026Privileged Information Distillation for Language Modelscited by 90
  13. 2025ARE: Scaling Up Agent Environments and Evaluationscited by 30
  14. 2026Benchmarking LLM Tool-Use in the Wildcited by 17
  15. 2025TurnBench-MS: A Benchmark for Evaluating Multi-Turn, Multi-Step Reasoning in Large Language Modelscited by 13

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 4.

4 papers.

  1. 2026RetireOPD: Self-Retiring On-Policy Distillation for Agentic Reinforcement Learningcited by 3
  2. 2026Eliciting Weak-to-Strong Generalization with On-Policy Reverse Distillationcited by 2
  3. 2026Critical-State RL: Diagnosing Trainable States for Multi-Turn Tool Usecited by 0
  4. 2026GraphOPD: Graph-Augmented On-Policy Distillation for LLM Agentscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Coverage, Not Targeting: A Structural Regime in Multi-Turn Agent Credit Assignment
  2. 2026How Much, Then Where: Credit-Conserving Action-to-Token Allocation for Multi-Turn Agent Reinforcement Learning
  3. 2026TCPO: Turn-Level Credit Policy Optimization
  4. 2026VETTA: Coordinating Turn- and Token-Level Credit Assignment for Multi-Turn LLM Agents
  5. 2026AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning
  6. 2026VICT: Verifier-Instrumented Credit Tracing for Long-Horizon LLM Agent Reinforcement Learning
  7. 2026Learning from Own Solutions: Self-Conditioned Credit Assignment for Reinforcement Learning with Verifiable Rewards
  8. 2026GEAR: Granularity-Adaptive Advantage Reweighting for LLM Agents via Self-Distillation