ACL 2026 Selects Paper On Self-Correcting Reward Models For Oral

Excited to share that our paper: “Teach a Reward Model to Correct Itself: Reward Guided Adversarial Failure Discovery for Robust Reward Modeling” has been selected as an Oral at #ACL2026 🎉 #ACL Reward models are increasingly the hidden control system behind modern AI alignment. But what happens when the reward model itself gets hacked? In this work, we train reward models to actively discover their own blind spots through adversarial failure discovery, improving robustness against reward hacking and distribution shifts. This is part of a broader direction my lab has been exploring recently on mitigating reward hacking, robust alignment, and building AI systems that can reason about their own failures rather than merely optimize superficial rewards. Paper: arXiv link https://arxiv.org/abs/2507.06419 Huge credit to the @PankayarajP who made this possible. He is on the job market this season!! Stay tuned — we’ll share a deeper technical blog post soon.