Snorkel AI proposes milestone-based evaluation to pinpoint long-horizon agent bottlenecks
ajratner · x · 2026-08-06
Evaluating AI agents on long-horizon tasks with a simple pass/fail score acts as a black box, failing to reveal which specific step caused the failure. Snorkel AI proposes a milestone-based evaluation and training method.
Core insights and value:
- Granular Diagnostics: Transforms a single terminal score into a per-phase diagnostic signal to pinpoint exact breakpoints in long trajectories.
- Optimized Credit Assignment: Converts one terminal reward into actionable credit that agents can actually learn from.
- Preserving Intermediate Progress: Keeps partially successful behaviors available for targeted data collection and future reinforcement learning, even if the final task fails.
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