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Unlocking the Power of Multi-Agent LLM for Reasoning: From Lazy Agents to Deliberation

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 5.5Download Report PDF
LLM Reasoning; Multi-agent LLMs
Abstract:

Large Language Models (LLMs) trained with reinforcement learning and verifiable rewards have achieved strong results on complex reasoning tasks. Recent work extends this paradigm to a multi-agent setting, where a meta-thinking agent proposes plans and monitors progress while a reasoning agent executes subtasks through sequential conversational turns. Despite promising performance, we identify a critical limitation: lazy agent behavior, in which one agent dominates while the other contributes little, undermining collaboration and collapsing the setup to an ineffective single agent. In this paper, we first provide a theoretical analysis showing why lazy behavior naturally arises in multi-agent reasoning. We then introduce a stable and efficient method for measuring causal influence, helping mitigate this issue. Finally, as collaboration intensifies, the reasoning agent risks getting lost in multi-turn interactions and trapped by previous noisy responses. To counter this, we propose a verifiable reward mechanism that encourages deliberation by allowing the reasoning agent to discard noisy outputs, consolidate instructions, and restart its reasoning process when necessary. Extensive experiments demonstrate that our framework alleviates lazy agent behavior and unlocks the full potential of multi-agent framework for complex reasoning tasks.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
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Overview

Overall Novelty Assessment

The paper addresses lazy agent behavior in multi-agent reinforcement learning for complex reasoning, proposing theoretical analysis and mitigation strategies. It resides in the Multi-Agent Debate and Deliberation leaf under LLM-Based Multi-Agent Reasoning Systems, which contains only three papers total. This is a relatively sparse research direction within the broader taxonomy of fifty papers across thirty-six topics, suggesting the specific problem of lazy agents in meta-thinking/reasoning agent setups has received limited focused attention. The work targets a niche but emerging area where multi-turn collaborative reasoning can degrade into single-agent dominance.

The taxonomy reveals neighboring leaves focused on role-playing collaboration, tool-integrated systems, and meta-cognitive frameworks, all within the LLM-based reasoning branch. The paper's emphasis on causal influence measurement and restart mechanisms connects it to meta-cognitive monitoring themes, yet its focus on lazy behavior and deliberation differs from pure role assignment or tool integration approaches. The broader Reinforcement Learning for Multi-Agent Reasoning Enhancement branch includes RL-based post-training and self-play methods, but these typically do not address agent passivity or restart strategies. The work thus bridges deliberation frameworks with RL optimization challenges in a way that neighboring leaves do not explicitly cover.

Among sixteen candidates examined, none clearly refute the three core contributions. The theoretical analysis of lazy behavior examined three candidates with zero refutations, the Shapley-inspired causal influence method examined ten candidates with zero refutations, and the verifiable reward mechanism for restart behavior examined three candidates with zero refutations. This limited search scope suggests that within the top semantic matches and citation expansions, no prior work directly overlaps with the specific combination of lazy agent diagnosis, causal influence measurement, and restart-based deliberation. However, the small candidate pool means the analysis does not exhaustively cover all potentially relevant multi-agent RL or LLM collaboration literature.

Based on the sixteen candidates examined, the work appears to introduce novel mechanisms for a specific failure mode in multi-agent reasoning. The sparse taxonomy leaf and absence of refuting candidates suggest the contributions are not widely addressed in closely related prior work. However, the limited search scope leaves open the possibility that broader MARL or LLM literature contains relevant techniques not captured by top-K semantic retrieval. The analysis provides evidence of novelty within the examined scope but does not constitute an exhaustive field survey.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
16
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: Multi-agent reinforcement learning for complex reasoning tasks. The field encompasses a diverse set of approaches organized into six main branches. Multi-Agent Coordination and Communication Mechanisms explores how agents share information and align their actions, often through learned protocols or emergent communication strategies. Hierarchical and Structured Multi-Agent Learning focuses on decomposing tasks into subtasks and organizing agents into layered architectures, enabling scalable solutions to intricate problems. LLM-Based Multi-Agent Reasoning Systems leverages large language models to enable agents to perform sophisticated deliberation, debate, and collaborative inference, as seen in works like Multi Agent Debate Review[18] and Group Deliberation Reasoning[49]. Reinforcement Learning for Multi-Agent Reasoning Enhancement investigates how RL techniques can refine reasoning capabilities, including policy optimization and reward shaping. Domain-Specific Multi-Agent Applications targets particular problem settings such as medical diagnosis, navigation, and knowledge graph reasoning, while Explainability, Analysis, and Theoretical Foundations addresses interpretability, robustness, and formal guarantees. Within the LLM-based reasoning branch, a particularly active line of work examines multi-agent debate and deliberation frameworks, where agents iteratively refine their outputs through structured exchanges. Lazy Agents to Deliberation[0] sits squarely in this cluster, emphasizing mechanisms that transition agents from passive or minimal-effort states into active deliberative processes. This contrasts with approaches like Mixture of Minds[3], which blends diverse reasoning strategies without necessarily requiring iterative debate, and Group Deliberation Reasoning[49], which focuses on collective decision-making protocols. A central trade-off in this area involves balancing the computational overhead of multi-round interactions against the quality gains from collaborative refinement. Open questions include how to dynamically allocate deliberation effort, integrate heterogeneous agent capabilities, and ensure that debate mechanisms generalize across reasoning domains beyond their initial training settings.

Claimed Contributions

Theoretical analysis of lazy agent behavior in multi-turn GRPO

The authors provide a theoretical analysis showing that the normalization term in multi-turn GRPO creates a structural bias favoring trajectories with fewer turns, which leads to lazy agent behavior where one agent dominates while the other contributes minimally.

3 retrieved papers
Shapley-inspired causal influence measurement method

The authors introduce a stable and efficient method for measuring causal influence by grouping semantically similar steps across rollouts and averaging their influence scores, avoiding additional sampling while producing robust estimates during online training.

10 retrieved papers
Verifiable reward mechanism for restart behavior

The authors design a verifiable reward mechanism that trains the reasoning agent to adaptively discard prior outputs, re-aggregate instructions, and restart reasoning when necessary, enabling recovery from errors in multi-turn interactions.

3 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Theoretical analysis of lazy agent behavior in multi-turn GRPO

The authors provide a theoretical analysis showing that the normalization term in multi-turn GRPO creates a structural bias favoring trajectories with fewer turns, which leads to lazy agent behavior where one agent dominates while the other contributes minimally.

Contribution

Shapley-inspired causal influence measurement method

The authors introduce a stable and efficient method for measuring causal influence by grouping semantically similar steps across rollouts and averaging their influence scores, avoiding additional sampling while producing robust estimates during online training.

Contribution

Verifiable reward mechanism for restart behavior

The authors design a verifiable reward mechanism that trains the reasoning agent to adaptively discard prior outputs, re-aggregate instructions, and restart reasoning when necessary, enabling recovery from errors in multi-turn interactions.

Table of Contents