A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
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Curriculum temperature for knowledge distillation (CTKD) is an easy-to-use plug-in technique that improves existing knowledge distillation frameworks with negligible additional cost.
S. Motwani, Alesia Ivanova, Ziyang Cai, et al.
Introduces a scalable method using curriculum RL on synthetically composed short-horizon data to boost long-horizon reasoning, achieving up to 2.06x accuracy gains on competition-level benchmarks.
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OLMo 2 introduces improved open-source language models with a specialized data mix and late-stage curriculum training.
Hao Liang, Qihan Lin, Zhaoyang Han, et al.
Introduces K12-KGraph, a curriculum-aligned knowledge graph from Chinese textbooks, with benchmark and training data to improve LLMs' curriculum cognition.
Eduardo Mosqueira-Rey, Elena Hernández-Pereira, David Alonso-Ríos, et al.
This paper reviews human-in-the-loop machine learning, clarifying definitions and boundaries between active learning, interactive ML, machine teaching, curriculum learning, and explainable AI.