Training data needs a real go/no-go gate before training [D]

<!-- SC_OFF --><div class="md"><p>Hi, <a href="/r/MachineLearning">r/MachineLearning</a>: has much research been done in large-scale training scenarios where undesirable data has

<!-- SC_OFF --><div class="md"><p>everyone's focused on the benchmarks (14 state of the art evals, beats mythos on terminal-bench, etc) but i think the actual story of 5.5 is what

<!-- SC_OFF --><div class="md"><p>Disclosure: I built LabelSets (labelsets.ai). Sharing the technical approach behind how we score dataset quality.</p> <p>THE PROBLEM</p> <p>Most

<blockquote> <p>A controlled study finds that data distribution, not just volume, dictates LLM capability. Benchmark-aligned training inflates scores but creates narrow, brittle

<!-- SC_OFF --><div class="md"><p>Public datasets on HF or Kaggle can sometimes be too generic, wrong domain, wrong schema, outdated, or just not enough volume to generalize

<!-- SC_OFF --><div class="md"><p>Just went through a Stanford paper that tracked 51 actual AI deployments - not surveys or sentiment polls, real production systems across 41

<!-- SC_OFF --><div class="md"><p>Built a third-party quality rating system for ML datasets. Multi-oracle (7 scorers across 5 algorithm families), conformal prediction intervals

<blockquote> <p><strong>TL;DR:</strong> Model alignment ≠ agent security. The gap between a trained model and a governed agent is where the next wave of enterprise AI incidents