Teach a Reward Model to Correct Itself: Reward Guided Adversarial Failure Discovery for Robust Reward Modeling

2026-06-08 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Expert, extended

Summary

REFORM is a novel, self-improving framework designed to enhance the robustness of reward models (RMs) used for aligning large language models (LLMs). It addresses the common issue of RMs failing under distributional shifts or adversarial perturbations, which often stems from complex human preferences and limited dataset coverage. REFORM introduces a preference-distribution-agnostic method that leverages reward-guided controlled decoding to discover failure modes by generating falsely scored responses. These adversarial examples are then used to augment the training data, correcting the RM's misaligned behavior. Evaluated on the Anthropic Helpful-Harmless (HH) and PKU Beavertails datasets, REFORM significantly improves RM robustness without compromising reward quality. It maintains performance in direct evaluation and across downstream policy training methods like Best-of-N (BoN), Proximal Policy Optimization (PPO), and Direct Preference Optimization (DPO), further enhancing alignment by mitigating spurious correlations.

Key takeaway

For Machine Learning Engineers focused on robust LLM alignment, traditional reward models often exhibit vulnerabilities to adversarial inputs and distributional shifts. You should consider integrating REFORM to automatically discover and correct reward model mis-specifications. This framework allows your reward model to self-improve by generating and learning from its own failure modes, enhancing robustness and alignment quality across methods like BoN, PPO, and DPO, without compromising overall performance.

Key insights

Reward models can self-improve robustness by generating and learning from their own adversarial failure modes.

Principles

• Reward model failures are often model-dependent.
• Controlled decoding finds mis-specifications tractably.
• Targeted data augmentation boosts robustness.

Method

REFORM guides controlled decoding using the reward model itself to generate class-consistent, incorrectly scored responses. These failure modes augment training data, then the reward model is fine-tuned.

In practice

• Generate false negatives and false positives.
• Augment training data with 5% influential failure variants.
• Integrate with BoN, PPO, or DPO alignment.

Topics

• Reward Modeling
• LLM Alignment
• Adversarial Examples
• Controlled Decoding
• Data Augmentation
• Model Robustness

Code references

• pankayaraj/REFORM

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.