Beyond Single Scores: Collaborative AI Improves Reasoning in Complex Tasks

Author: Denis Avetisyan

A new approach leverages multiple AI agents to evaluate performance, significantly boosting the reasoning capabilities of large language models and enhancing the reliability of feedback.

RewardBench departs from conventional methods employing fixed reward functions by implementing a multi-agent system, thereby constructing an extensible and dynamically adaptive intelligent reward function.

This paper introduces a collaborative reward modeling framework for multi-agent systems that improves interpretability and robustness in reinforcement learning from human feedback.

Optimizing large language models via Reinforcement Learning from Human Feedback (RLHF) remains challenging due to the difficulty of designing reward functions that accurately capture complex, multi-faceted preferences. This paper introduces a novel framework, ‘Multi-Agent Collaborative Reward Design for Enhancing Reasoning in Reinforcement Learning’, which replaces a single reward model with a coordinated team of specialized evaluators. By decomposing preference assessment into domain-specific agents and fusing their signals, this approach enhances both the interpretability and robustness of the reward signal, leading to improved reasoning capabilities. Could this collaborative paradigm unlock more stable and transparent optimization pathways for increasingly sophisticated language models?

The Alignment Problem: Defining Desirable LLM Behavior

Despite remarkable advances in generating human-quality text, large language models consistently struggle to accurately reflect the subtleties of human intent. While these models can produce grammatically correct and contextually relevant responses, ensuring alignment with complex preferences – encompassing factors like helpfulness, honesty, and harmlessness – presents a significant hurdle. The core difficulty lies not in the ability to generate text, but in defining and instilling the nuanced criteria that constitute truly desirable model behavior. Current approaches often fall short because human preferences are rarely monolithic; they are shaped by individual values, situational context, and implicit expectations that are difficult to fully capture in training data or objective functions. This misalignment can manifest as outputs that, while technically proficient, are unhelpful, biased, or even harmful, highlighting the critical need for more sophisticated alignment strategies.

Current approaches to aligning large language models frequently depend on a single reward model, built from human evaluations of generated text. This system creates a significant bottleneck because subjective judgements are inherently complex and multi-layered; distilling these nuances into a single scalar reward proves remarkably difficult. The resulting model often struggles to capture the full spectrum of desirable qualities, such as helpfulness, honesty, and harmlessness, leading to outputs that satisfy the reward signal but fail to truly reflect human intent. This limitation hinders the development of LLMs capable of genuinely understanding and responding to complex, open-ended requests, necessitating more sophisticated feedback mechanisms.

The pursuit of truly helpful and harmless large language models is complicated by the inherent difficulty of defining “desirable behavior.” Current alignment strategies frequently lean on a single reward model, effectively asking it to encapsulate the entirety of human preference. However, this approach overlooks the multi-faceted nature of what humans actually want – a response might be factually correct, creatively insightful, and ethically sound, all simultaneously. Reducing these diverse, and sometimes competing, qualities to a single scalar value inevitably leads to trade-offs and suboptimal performance; a model optimized for succinctness, for instance, might sacrifice nuance or completeness. Consequently, LLMs often struggle with tasks requiring a balance of attributes, highlighting the limitations of relying on a monolithic assessment of quality and suggesting a need for more sophisticated, multi-objective alignment techniques.

This decomposition illustrates the distinct roles of reward in collaborative tasks.

Decomposition Through Collaboration: A Multi-Agent Reward Framework

Collaborative Reward Modeling (CRM) departs from traditional reward systems by utilizing a distributed evaluation process. Instead of relying on a single reward function, CRM employs multiple evaluators, each trained to assess a specific facet of output quality – such as helpfulness, truthfulness, or harmlessness. This decomposition of the reward signal into distinct dimensions allows for a more granular and nuanced assessment of Large Language Model (LLM) outputs. By assigning specialized evaluators to these discrete quality attributes, CRM aims to capture a more comprehensive understanding of performance than can be achieved with a monolithic reward model, ultimately improving the alignment of LLMs with complex human preferences.

The collaborative framework utilizes both Global Evaluators and Specialized Evaluators to assess Large Language Model (LLM) outputs. Global Evaluators provide a holistic judgment of overall quality, while Specialized Evaluators focus on specific, predefined criteria such as factual accuracy, coherence, or relevance to a given prompt. These evaluators do not operate independently; rather, their individual assessments are aggregated – typically through a weighted averaging or more complex fusion method – to generate a composite reward signal. This coordinated approach allows for a more nuanced and comprehensive evaluation than would be possible with a single evaluator, capturing a wider range of quality dimensions and mitigating biases inherent in any single assessment metric.

The aggregation of multiple evaluator perspectives in Collaborative Reward Modeling (CRM) generates a more statistically significant and less biased reward signal than single-evaluator methods. This is achieved by combining the outputs of diverse agents, each trained to assess LLM outputs based on differing criteria or quality dimensions. The resulting composite reward function reduces the impact of individual evaluator idiosyncrasies and enhances the reliability of the signal used during policy optimization. Consequently, the LLM is guided toward behaviors that consistently satisfy a broader range of quality expectations, leading to improved generalization and performance across varied tasks and inputs.

MARM: Operationalizing Collaborative Reward Modeling

The Multi-Agent Reward Model (MARM) operationalizes the broader concept of Customer Relationship Management (CRM) through a system of specialized agents. These agents-including the Data Analyzer, Data Optimizer, Quality Assessor, and Data Synthesizer-work in concert to evaluate and refine CRM strategies. The Data Analyzer focuses on the consistency of reward signals, while the Data Optimizer aims to maximize positive outcomes. The Quality Assessor validates the accuracy and relevance of data used in the model, and the Data Synthesizer generates additional training data to improve model performance. This agent-based approach allows for a decomposition of complex CRM evaluation into manageable, specialized tasks.

Within the Multi-Agent Reward Model (MARM), specialized agents provide distinct contributions to the evaluation process. The Data Analyzer agent continuously monitors the stability of the reward signal, identifying potential issues such as reward hacking or signal drift that could compromise the reliability of the CRM system. Concurrently, the Data Synthesizer agent addresses data scarcity by generating synthetic data, effectively augmenting the available supervision signal and improving model generalization, particularly in scenarios with limited real-world examples. This combined functionality ensures a robust and adaptable evaluation framework.

The Multi-Agent Reward Model (MARM) achieves a holistic and reliable evaluation process through the strategic deployment of specialized agents. Each agent – including the Data Analyzer, Data Optimizer, Quality Assessor, and Data Synthesizer – contributes a distinct perspective to the assessment of reward signals and data quality. This diversity mitigates the risk of bias inherent in single-point evaluations; discrepancies identified by one agent can be cross-validated or investigated by others. Furthermore, the combined output of these agents provides a more comprehensive understanding of system performance than would be achievable with a monolithic evaluation approach, leading to increased confidence in the results and more robust decision-making.

Empirical Validation: Demonstrating CRM’s Performance Gains

Central to validating the capabilities of the proposed Collaborative Reasoning Mechanism (CRM) is its rigorous testing against established benchmarks like RewardBench and GSM8K. These benchmarks aren’t merely scoring tools; they provide a standardized landscape for evaluating the quality of reward signals – the very foundation upon which large language model (LLM) policies are optimized. RewardBench assesses an LLM’s ability to align with human preferences, while GSM8K challenges its mathematical reasoning and problem-solving skills. By consistently performing well on these diverse tests, CRM demonstrates its capacity to not only process information but also to derive meaningful insights and make well-informed decisions, ultimately proving its effectiveness in enhancing LLM performance and reliability.

The refinement of Large Language Model (LLM) policies benefits significantly from established benchmarks and advanced optimization techniques. Utilizing tools like RewardBench and GSM8K allows for quantifiable assessment of an LLM’s reasoning capabilities, while policy optimization methods, particularly those incorporating Generalized Advantage Estimation (GAE), enable targeted improvements. GAE facilitates a more accurate evaluation of actions by estimating the long-term advantage of taking specific steps, leading to more effective policy updates. This iterative process, fueled by benchmark data and sophisticated estimation, allows researchers to fine-tune LLM behavior, boosting performance on complex reasoning tasks and ultimately creating more robust and reliable artificial intelligence systems.

Evaluations utilizing the CRM framework demonstrate a clear correlation between agent configuration and reasoning performance on established benchmarks. Specifically, a four-agent setup yielded a reasoning accuracy of 0.690 on RewardBench and 27.60% on the GSM8K dataset. These results represent a substantial improvement over a baseline two-agent configuration, which achieved 0.639 on RewardBench and 22.16% on GSM8K. Further gains were also observed when contrasted with a three-agent system, which registered scores of 0.689 for RewardBench and 22.87% for GSM8K, highlighting the benefits of increased collaborative reasoning within the CRM architecture.

The pursuit of robust and interpretable reward signals is paramount in reinforcement learning, a principle clearly demonstrated by this research into collaborative reward modeling. The framework’s reliance on multiple specialized agents echoes a commitment to rigorous evaluation, ensuring that assessments are not based on singular, potentially flawed perspectives. As Grace Hopper once stated, “It’s easier to ask forgiveness than it is to get permission.” This sentiment aligns with the paper’s innovative approach; by embracing diverse evaluations, the system inherently acknowledges the complexities of reasoning and avoids premature commitments to simplistic reward structures. The collaborative approach directly addresses the limitations of single reward models, striving for provable accuracy rather than merely functional performance on benchmark tests.

What’s Next?

The proliferation of agents evaluating agents-a meta-evaluation, if one will-highlights a fundamental tension. While this work demonstrates improvement via distributed critique, it merely shifts the problem of reward specification, not solves it. The consistency-and therefore, the truth-of the collaborative reward remains contingent on the individual agents’ internal logic. Achieving provable correctness in this multi-agent system is not simply a matter of increasing agent numbers, but demands a formal verification of each agent’s evaluation criteria. The current paradigm risks exchanging a single point of failure for a distributed, yet equally opaque, one.

Future work must address the inherent subjectivity embedded within even ‘specialized’ agents. A truly robust system requires grounding evaluations in axiomatic principles, moving beyond empirical observation to deductive reasoning. The RewardBench benchmark, while valuable, assesses performance against existing human preferences – a circularity that limits fundamental progress. The field needs benchmarks that test for logical consistency, not merely alignment with current, fallible, human intuition.

Ultimately, the pursuit of ‘interpretable’ reward models is a pragmatic compromise. The elegance lies not in understanding why a model prefers one outcome over another, but in constructing a reward function that is demonstrably, mathematically, correct. The current trajectory favors empirical refinement; a shift towards formal verification is not merely desirable, it is logically imperative.

Original article: https://arxiv.org/pdf/2511.16202.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

The Alignment Problem: Defining Desirable LLM Behavior

Decomposition Through Collaboration: A Multi-Agent Reward Framework

MARM: Operationalizing Collaborative Reward Modeling

Empirical Validation: Demonstrating CRM’s Performance Gains

What’s Next?

See also: