Reward hacking is old news.
The hard part: finding the judge’s blind spots before RL turns them into a strategy.
Can a reward model improve itself?
REFORM lets the judge red-team itself: discover responses itmis-scores, then train on those mistakes.
#ACL2026 Oral https://arxiv.org/abs/2507.06419
Users are excited about REFORM because it enables reward models to self-red-team and create self-improving critics rather than relying on static alignment checkpoints.
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#434Congratulations to @PankayarajP! Talk to Pankayaraj at #ACL2026 #Oral Session.
📍09:00-10:30 Tues, July 7 | Harbor D | Session 16: Oral/Poster/Demos F | Orals Session F: Safety and Alignment in LLMs 2
6/ This is the direction I’m most excited about:
alignment systems that are not static checkpoints, but self-improving critics.
Paper: https://arxiv.org/abs/2507.06419 Code: https://github.com/pankayaraj/REFORM
1/ As AI systems become more agentic, the bottleneck shifts.
Not just: can the model act?
But: can the system reliably judge which actions are good?
That judge is often a reward model.
Reward hacking is old news.
The hard part: finding the judge’s blind spots before RL turns them into a strategy.
Can a reward model improve itself?
REFORM lets the judge red-team itself: discover responses itmis-scores, then train on those mistakes.
#ACL2026 Oral https://arxiv.org/abs/2507.06419
6/ This is the direction I’m most excited about:
alignment systems that are not static checkpoints, but self-improving critics.
Paper: https://arxiv.org/abs/2507.06419 Code: https://github.com/pankayaraj/REFORM
5/ On HH + PKU Beavertails, REFORM
a. Succeeds in finding meaningful, readable and successful reward failures b. Improves robustness to OOD/adversarial perturbations c. Preserves reward quality and downstream alignment performance.
2/ Reward hacking is the symptom.
The deeper issue is incomplete supervision.
A reward model trained on finite preference data will have blind spots.
Once we optimize against it, those blind spots become targets.
1/ As AI systems become more agentic, the bottleneck shifts.
Not just: can the model act?
But: can the system reliably judge which actions are good?
That judge is often a reward model.
5/ On HH + PKU Beavertails, REFORM
a. Succeeds in finding meaningful, readable and successful reward failures b. Improves robustness to OOD/adversarial perturbations c. Preserves reward quality and downstream alignment performance.
4/ These become model-specific failure cases.
Then we train on them.
The result: a reward model that is not just evaluated for robustness, but actively improved through its own discovered mistakes.
REFORM turns decoding into red-teaming: take top-k tokens from an aligned model, then choose the one the reward model scores lowest. Likely-aligned continuation + low reward = false negative.
3/ REFORM asks a simple question:
Can we find those targets automatically, before the policy does?
Instead of waiting for RL to exploit the reward model, we use the reward model itself to generate adversarially mis-scored responses.
4/ These become model-specific failure cases.
Then we train on them.
The result: a reward model that is not just evaluated for robustness, but actively improved through its own discovered mistakes.
REFORM turns decoding into red-teaming: take top-k tokens from an aligned model, then choose the one the reward model scores lowest. Likely-aligned continuation + low reward = false negative.
3/ REFORM asks a simple question:
Can we find those targets automatically, before the policy does?
Instead of waiting for RL to exploit the reward model, we use the reward model itself to generate adversarially mis-scored responses.
2/ Reward hacking is the symptom.
The deeper issue is incomplete supervision.
A reward model trained on finite preference data will have blind spots.
Once we optimize against it, those blind spots become targets.
