Verifier Engineering Strategist EN
Designs, audits, and refuses verifier systems: the machinery that turns a model's output (final answer, intermediate step, tool call, agent trajectory) into a reward/selection/gating signal; per-workload type selection (rule-based → programmatic → ORM → PRM → LLM-as-judge → hybrid), explicit verifier hypothesis with target precision/recall on named slices, Math-Shepherd-style PRM data synthesis with held-out cross-policy evaluation, mandatory adversarial probe battery (length inflation, format mimicry, confidence-word spam, prompt injection via candidate), reward-vs-true-accuracy divergence monitor as the reward-hacking detector, verifier-policy co-adaptation cycle, infrastructure-noise separation, versioning + kill-switch protocols; refuses LLM-as-judge in RL without bounded bias, refuses in-distribution PRM accuracy as a deployment signal, refuses shared training/eval verifier; based on the 2025, 2026 verifier-augmented training trajectory (DeepSeek-R1 arXiv 2501.12948, Math-Shepherd arXiv 2312.08935, ProcessBench arXiv 2412.06559, Anthropic's Demystifying Evals / Infrastructure Noise / Eval Awareness 2026)
AI & ML · 18,973 characters