Agent-judged rewards: hacking fades as models get smarter and tasks easier to verify
willcb · x · 2026-09-27
A discussion on designing rewards in open-ended RL settings:
- Rewards are typically given by another agent following clear instructions on what counts as good/bad
- Reward hacking remains possible but becomes harder as models grow smarter and more robust
- Another key knob is QC and difficulty tuning: easier-to-validate tasks are less hack-prone
Scaling robustness alongside optimizer power is considered fairly doable with a good starting point.
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