Contextual Counterfactual Credit Assignment for Multi-Agent Reinforcement Learning in LLM Collaboration

• URL: http://arxiv.org/abs/2603.06859v1
• Date: Fri, 06 Mar 2026 20:25:11 GMT
• Title: Contextual Counterfactual Credit Assignment for Multi-Agent Reinforcement Learning in LLM Collaboration
• Abstract summary: We introduce Contextual Counterfactual Credit Assignment (textbftextttC3)<n>Instead of distributing rewards across an entire episode, textbftextttC3 isolates the causal impact of individual messages.<n>textbftextttC3 improves terminal performance over established baselines.
• Score: 22.269718913202595
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Cooperative multi-agent reinforcement learning (MARL) systems powered by large language models (LLMs) are frequently optimized via sparse terminal-only feedback. This shared signal entangles upstream decisions, obstructing accurate decision-level credit assignment. To address this trajectory-level diffusion, we introduce Contextual Counterfactual Credit Assignment (\textbf{\texttt{C3}}). Instead of distributing rewards across an entire episode, \textbf{\texttt{C3}} isolates the causal impact of individual messages by freezing the exact transcript-derived context, evaluating context-matched alternatives via fixed-continuation replay, and applying a leave-one-out (LOO) baseline. This localized intervention extracts unbiased, low-variance marginal advantages for standard policy-gradient optimization. Evaluated across five mathematical and coding benchmarks under matched budgets, \textbf{\texttt{C3}} improves terminal performance over established baselines. Mechanistic diagnostics further show that these gains are accompanied by higher credit fidelity, lower contextual variance, and stronger inter-agent causal dependence. Our code is available at https://github.com/EIT-EAST-Lab/C3.

Related papers

• Learning Where Outcomes Change:Credit-Addressable Reasoning for Multimodal Geometry [105.89493240787861]
  We introduce credit-addressable reasoning, in which semantic units exposed during inference also define where learning compares alternatives and assigns credit.<n>We instantiate this principle with Code-CoT, which represents visual relations as line-addressable executable code, and organizes reasoning into typed events.<n>Across nine geometry benchmarks, CE-GRPO achieves an average accuracy of 76.04, outperforming Qwen3-VL-8B and trajectory-level GRPO by $8.09$ and 3.43 points, respectively.
  arXiv  Detail & Related papers  (2026-08-31T08:44:36Z)
• Shared Prefixes, Better Credit: Adaptive Routing for Multi-Agent Reasoning [72.49938949082117]
  Multi-agent reasoning (MAR) improves reasoning reliability through iterative solution exchange and refinement.<n>Existing adaptive MAR methods typically learn routing decisions from query-level labels or trajectory-level returns.<n>We propose TreeCredit, a shared-operator credit assignment framework for efficient adaptive MAR.
  arXiv  Detail & Related papers  (2026-08-03T14:22:23Z)
• APPO: Agentic Procedural Policy Optimization [28.730334202611257]
  We study agentic RL from two perspectives: textitwhere to branch and how to assign credit after branching<n>We propose APPO, which shifts branching and credit assignment from coarse interaction units to fine-grained decision points in the sequence.<n>APPO consistently improves strong agentic RL baselines by nearly 4 points, while keeping efficient tool-calls and maintaining behavior interpretability.
  arXiv  Detail & Related papers  (2026-06-10T17:47:07Z)
• TRACE: A Unified Rollout Budget Allocation Framework for Efficient Agentic Reinforcement Learning [52.67756371231985]
  Reinforcement learning with verifiable rewards (RLVR) is a promising approach for enhancing reasoning and agentic behavior in large language models.<n>We introduce Tree Rollout Allocation for Contrastive Exploration (TRACE), a unified rollout allocation framework.<n>Technically, TRACE allocates rollout budget to both prompt roots and intermediate prefixes that are most likely to yield mixed terminal rewards.
  arXiv  Detail & Related papers  (2026-06-09T17:16:03Z)
• TAPO: Tool-Aware Policy Optimization via Credit Transfer for Multimodal Search Agents [54.08846865906602]
  We characterize credit misassignment as a systematic failure mode of GRPO in tool-augmented multimodal search agents.<n>We propose Tool-Aware Policy Optimization (TAPO), which exploits the parameter-determinism property of information-acquisition tools.
  arXiv  Detail & Related papers  (2026-06-04T07:15:43Z)
• Unifying Temporal and Structural Credit Assignment in LLM-Based Multi-Agent Prompt Optimization [10.37712840622514]
  Multi-Agent Systems (MAS) empower Large Language Models to tackle complex reasoning computation tasks.<n>Existing black-boxs struggle to attribute trajectory-level failure to specific local components.<n>We argue that tractable MAS optimization needs structural inductive biases to disentangle error signals.
  arXiv  Detail & Related papers  (2026-05-28T16:57:57Z)
• DISA: Offline Importance Sampling for Distribution-Matching LLM-RL [56.9445657766829]
  We introduce DISA, which moves this calibration problem outside the RL loop.<n> DISA draws proposal trajectories offline, estimates the partition function via importance sampling, and freezes the resulting partition-function estimate.<n>On two open-weight backbones across six math and three code benchmarks, DISA matches or exceeds the online-coupled distribution-matching baseline Flow.
  arXiv  Detail & Related papers  (2026-05-17T07:14:44Z)
• Reducing Credit Assignment Variance via Counterfactual Reasoning Paths [11.514388061694268]
  Reinforcement learning for multi-step reasoning with large language models (LLMs) often relies on sparse terminal rewards.<n>We introduce a counterfactual comparison-based credit assignment framework, which samples multiple reasoning trajectories under the same input.<n>We construct an implicit process-level advantage estimator that transforms sparse terminal rewards into step-sensitive learning signals.
  arXiv  Detail & Related papers  (2026-04-20T13:33:54Z)
• Counterfactual Credit Policy Optimization for Multi-Agent Collaboration [27.260904103460664]
  Collaborative multi-agent large language models (LLMs) can solve complex reasoning tasks by decomposing roles and aggregating diverse hypotheses.<n>We introduce Counterfactual Credit Policy Optimization (CCPO), a framework that assigns agent-specific learning signals.<n> CCPO builds dynamic counterfactual baselines that simulate outcomes with an agent's contribution removed.
  arXiv  Detail & Related papers  (2026-03-23T04:35:02Z)
• Balancing Faithfulness and Performance in Reasoning via Multi-Listener Soft Execution [79.98699884805636]
  Reasoning Execution by Multiple Listeners (REMUL) is a multi-party reinforcement learning approach.<n>REMUL builds on the hypothesis that reasoning traces which other parties can follow will be more faithful.<n>Speakers are rewarded for producing reasoning that is clear to listeners.
  arXiv  Detail & Related papers  (2026-02-18T02:55:55Z)
• SimGR: Escaping the Pitfalls of Generative Decoding in LLM-based Recommendation [68.00727783181289]
  A core objective in recommender systems is to accurately model the distribution of user preferences over items to enable personalized recommendations.<n>We observe that existing methods inevitably introduce systematic bias when estimating item-level preference distributions.<n>We propose textbfSimply textbfGenerative textbfRecommendation (textbfSimGR), a framework that directly models item-level preference distributions in a shared latent space.
  arXiv  Detail & Related papers  (2026-02-08T07:26:52Z)
• Diffusion-State Policy Optimization for Masked Diffusion Language Models [23.931675509910047]
  Masked diffusion language models generate by iteratively filling masked tokens over multiple denoising steps.<n>We propose DiSPO, a plug-in credit-assignment layer that directly optimize intermediate filling decisions.
  arXiv  Detail & Related papers  (2026-02-06T07:47:22Z)
• CORE: Context-Robust Remasking for Diffusion Language Models [51.59514489363897]
  We propose Context-Robust Remasking (CORE), a training-free framework for inference-time revision.<n>Rather than trusting static token probabilities, CORE identifies context-brittle tokens by probing their sensitivity to targeted masked-context perturbations.<n>On LLaDA-8B-Base, CORE delivers consistent improvements across reasoning and code benchmarks, outperforming compute-matched baselines and improving MBPP by up to 9.2 percentage points.
  arXiv  Detail & Related papers  (2026-02-04T00:12:30Z)
• Prism: Efficient Test-Time Scaling via Hierarchical Search and Self-Verification for Discrete Diffusion Language Models [96.0074341403456]
  Inference-time compute has re-emerged as a practical way to improve LLM reasoning.<n>Most test-time scaling (TTS) algorithms rely on autoregressive decoding.<n>We propose Prism, an efficient TTS framework for dLLMs.
  arXiv  Detail & Related papers  (2026-02-02T09:14:51Z)
• In-Token Rationality Optimization: Towards Accurate and Concise LLM Reasoning via Self-Feedback [38.915062716409686]
  InTRO is a new framework that enables both token-level exploration and self-feedback for accurate and concise reasoning.<n>InTRO consistently outperforms other baselines, raising solution accuracy by up to 20% relative to the base model.<n>Its chains of thought are notably more concise, exhibiting reduced verbosity.
  arXiv  Detail & Related papers  (2025-11-13T01:47:06Z)
• GRAPE: Let GPRO Supervise Query Rewriting by Ranking for Retrieval [19.73916326078242]
  The CLIP model has become a cornerstone of large-scale retrieval systems by aligning text and image data in a unified embedding space.<n>To avoid costly retraining, existing methods mainly adopt query-rewriting strategies with large language models (LLMs)<n>We address this challenge with GRAPE, a plug-and-play enhancement approach that incorporates ranking signals into retrieval-guided query rewriting.
  arXiv  Detail & Related papers  (2025-09-27T15:36:59Z)
• Reinforcing Video Reasoning with Focused Thinking [65.85683941058916]
  We propose TW-GRPO, a novel framework that enhances visual reasoning with focused thinking and dense reward granularity.<n>Specifically, we employ a token weighting mechanism that prioritizes tokens with high informational density.<n>We also reformulate RL training by shifting from single-choice to multi-choice QA tasks.
  arXiv  Detail & Related papers  (2025-05-30T15:42:19Z)
• Redistributing Rewards Across Time and Agents for Multi-Agent Reinforcement Learning [14.852334980733369]
  Credit assignmen, disentangling each agent's contribution to a shared reward, is a critical challenge in cooperative multi-agent reinforcement learning.<n>We introduce Temporal-Agent Reward Redistribution (TAR$2$), an approach that decouples credit modeling from this constraint.<n>We demonstrate that this method is equivalent to a valid Potential-Based Reward Shaping (PBRS), which guarantees the optimal policy is preserved regardless of model accuracy.
  arXiv  Detail & Related papers  (2025-02-07T12:07:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.