mlsanityData to 7 Oct 2026

Design Conditions for Intra-Group Learning of Sequence-Level Rewards: Token Gradient Cancellation

arXiv 2604.13088 ↗First posted 4 Apr 2026cs.LG cs.AIcited by 0

On the map: LLM reasoning → Reinforcement learning for reasoning

Abstract (arXiv)

Reinforcement learning for multi-step reasoning with large language models (LLMs) typically relies on sparse terminal rewards, which creates a poorly conditioned credit-assignment problem: the final feedback is propagated uniformly across all intermediate decisions. This leads to high gradient variance, unstable training, and many ineffective updates, ultimately limiting sustained model improvement. We propose a counterfactual-comparison framework for credit assignment. For each input, the framework samples multiple reasoning trajectories and treats their differences as implicit approximations to alternative decisions. This yields an implicit process-level advantage estimator that converts sparse terminal rewards into step-sensitive learning signals. Building on this framework, we introduce Implicit Behavior Policy Optimization (IBPO), which substantially improves training stability and the performance ceiling on mathematical and code-reasoning benchmarks. Our results point to a promising direction for unlocking the reasoning potential of LLMs.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Reinforcement learning for reasoning, one of 17 directions in LLM reasoning. Builds on 8 of its 10 references that are in the map. First posted on arXiv 4 Apr 2026.

Builds on

8 of 10 references.

Closest ancestors · 3

Same direction, newest first

  1. 2025SSPO: Subsentence-level Policy Optimization6 Nov
  2. 2025DCPO: Dynamic Clipping Policy Optimization2 Sep
  3. 2025DAPO: An Open-Source LLM Reinforcement Learning System at Scale18 Mar

Other references · 5

  1. 2024DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Modelscited by 8,851
  2. 2025Qwen3 Technical Reportcited by 8,195
  3. 2024LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Codecited by 1,906
  4. 2025Group Sequence Policy Optimizationcited by 788
  5. 2025MathArena: Evaluating LLMs on Uncontaminated Math Competitionscited by 275

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Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2026Reducing Credit Assignment Variance via Counterfactual Reasoning Paths
  2. 2026InT: Self-Proposed Interventions Enable Credit Assignment in LLM Reasoning
  3. 2026Semifactual Credit-Augmented Policy Optimization
  4. 2026Credit Assignment with Resets in Language Model Reasoning
  5. 2026Discovering Process-Outcome Credit in Multi-Step LLM Reasoning
  6. 2025Exploiting Tree Structure for Credit Assignment in RL Training of LLMs
  7. 2025Well Begun, Half Done: Reinforcement Learning with Prefix Optimization for LLM Reasoning
  8. 2025Token-Efficient RL for LLM Reasoning