# Mechanism Design for LLM Fine-tuning with Multiple Reward Models

arXiv: https://arxiv.org/abs/2405.16276 · HTML: https://mlsanity.com/paper/2405.16276/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 May 2024.

## Built on by (most cited first, 18 of 18)

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- [Online Learning from Strategic Human Feedback in LLM Fine-Tuning](https://mlsanity.com/paper/2412.16834/) (2024)
- [Pay for The Second-Best Service: A Game-Theoretic Approach Against Dishonest LLM Providers](https://mlsanity.com/paper/2511.00847/) (2025)
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- [Beyond RLHF and NLHF: Population-Proportional Alignment under an Axiomatic Framework](https://mlsanity.com/paper/2506.05619/) (2025)
- [VickreyFeedback: Cost-efficient Data Construction for Reinforcement Learning from Human Feedback](https://mlsanity.com/paper/2409.18417/) (2024)
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- [The Battling Influencers Game: Nash Equilibria Structure of a Potential Game and Implications to Value Alignment](https://mlsanity.com/paper/2502.01127/) (2025)
- [Learning Truthful Mechanisms without Discretization](https://mlsanity.com/paper/2506.22911/) (2025)
- [Collaborating with GenAI: Incentives and Replacements](https://mlsanity.com/paper/2508.20213/) (2025)
- [Incentivizing High-Quality Human Annotations with Golden Questions](https://mlsanity.com/paper/2505.19134/) (2025)
- [Users as Annotators: LLM Preference Learning from Comparison Mode](https://mlsanity.com/paper/2510.13830/) (2025)
- [Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcing](https://mlsanity.com/paper/2605.24052/) (2026)
- [Algorithmic Impact Reveals the Hidden Social Choice Structure of Alignment](https://mlsanity.com/paper/2608.24046/) (2026)
- [AI Alignment through a Game-theoretic Lens: A Survey](https://mlsanity.com/paper/2608.27910/) (2026)

## Abstract (arXiv)

Fine-tuning large language models (LLMs) to aggregate multiple preferences has attracted considerable research attention. With aggregation algorithms advancing, a potential economic scenario arises where fine-tuning services are provided to agents with different preferences. In this context, agents may benefit from strategically misreporting their preferences, but this could harm the aggregation performance. This paper addresses such incentive issues by framing it as a mechanism design problem: an LLM provider determines the fine-tuning objective (training rule) and the pricing scheme (payment rule) for agents. We primarily focus on training rules that maximize social welfare subject to certain regularizations, referred to as SW-Max rules. First, we show that under most circumstances, truthful reporting is sub-optimal with simply a SW-Max rule, thereby highlighting the necessity of payments. Second, we extend the VCG payment to implement SW-Max rules in dominant-strategy incentive compatibility (DSIC). We characterize sufficient conditions for payment equivalence and derive the necessary conditions for a payment rule to implement a SW-Max rule in DSIC and other principles. Third, we demonstrate that our mechanism is approximately DSIC with perturbed input, showcasing its robustness against the inevitable errors in real-world applications. Experiments on real LLM training results further confirm the practical implications of our results.