Task Fidelity Scaling Laws — Kobie Crawdord, Snorkel
Jun 2, 2026 · 20:40
Kobie Crawford from Snorkel presents research on task fidelity scaling laws, showing that fine-tuning on high-quality agentic TerminalBench tasks yields a 5x improvement over low-quality tasks (6% vs. 1% uplift) with the same model, compute, and task count. Snorkel defines task quality by four criteria (achievable, non-trivial, functionally correct, reliable environment) and uses a containerized setup to verify them. Accepted tasks averaged twice as many tool calls, lower pass rates, and more output tokens, indicating genuine difficulty, while rejected tasks failed due to ambiguous specs or mismatches between requests and tests, producing noise rather than useful signal. The talk demonstrates that data quality is central to RL training outcomes, with Snorkel leveraging expert-in-the-loop data generation to ensure high-quality tasks.
