An in-depth look at how knowledge distillation transfers capabilities from large teacher models to smaller, efficient student models covering response-based, feature-based, and attention-based techniques.
Explores how reinforcement learning addresses the core limitations of standard LLM training for long-horizon tasks covering MDPs, policy gradients, RLHF, DPO, and hierarchical planning for multi-step decision-making.
Inter-annotator agreement is a core measure of data quality in machine learning. When multiple annotators label the same data, agreement levels reveal how consistently a task can be interpreted and how reliable the resulting labels are. Low agreement often indicates unclear guidelines, task ambiguity, or expertise mismatches rather than annotator error. Measuring and monitoring inter-annotator agreement helps teams detect label noise early, improve annotation design, and produce datasets that lead to more stable and generalizable models.
External value system and human oversight keep models aligned with your policies. We measure what you care about, then train toward it.
High quality outputs from subject matter experts with rapid turnaround. We handle hiring spikes and sustained velocity.
Automated Sourcing, vetting, matching, and interview scheduling to assemble elite teams quickly. 500K+ US talent.
