Scale AI unveils Sauron, an evidence-first verifier to cut RL reward errors
ScaleAI · hf · 2026-10-20
A paper from Scale AI and the University of Arizona, Sauron targets a pain point in rubric-based evaluation of long-horizon, semi-verifiable agentic tasks: evidence is spread across environment state and execution traces, and judge models may overlook it or accept unsupported claims — including an agent's own reports of success.
- Sauron is an evidence-first agentic verifier that investigates both state and trace, relying on a claim only after independently validating it against its source
- When rubric verdicts serve as RL rewards, such errors reinforce undesirable behavior; Sauron improves rubric-based evaluation accuracy and reduces reward errors
- Code open-sourced at github.com/rsanayei/sauron
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