More from Rise
Knowledge Distillation in Large Language Models
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.
Read article
Reinforcement Learning for Long-Horizon Tasks
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.
Read article
Inter-Annotator Agreement in Multi-Annotator Labeling Explained
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.
Read article04 / Work with Rise Data Labs
