Common Pitfalls in QA for RL Tasks at AI Labs
andersonbcdefg · x · 2026-08-03
After reviewing numerous task QA samples from AI labs, the author identified common patterns in poorly executed training tasks, particularly in reinforcement learning (RL):
- Unfair or brittle verifiers: To keep outputs in-band, tasks often use fragile checks like substring matching for non-deterministic outputs. Some verifiers unfairly require impossible or highly subjective information, triggering model hallucinations.
- Poor rollout analysis: Even with correct verifiers, better trajectories are often penalized simply because they deviate from the author's suspected "golden path."
- Lack of in-batch comparison: There is a frequent failure to check whether higher-scoring rollouts within the same batch are actually better than lower-scoring ones.
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