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提出一种协作学习框架，用多个异构小模型替换单个大模型进行推理（model slicing），并通过金字塔监督蒸馏改进跨模型协作，在多跳问答、数学推理、代码生成等任务上达到与大模型相当或更优的性能，且推理效率更高。
This paper explores using instruction-tuned LLMs to analyze collaborative student sensemaking in science learning, finding that reasoning-enabled prompting and knowledge-state diagnostics improve alignment with expert annotations.
This survey provides a comprehensive investigation of collaborative learning from Euclidean to graph-structured data, aiming to consolidate this emerging field and identify open challenges.
The paper proposes a robust dual-model collaborative random vector functional link network (KRPRVFL) to improve classification accuracy in the presence of noisy labels and outliers, leveraging kernel risk-sensitive mean p-power criterion and collaborative learning.
Introduces Collate, a training framework for collaborative neural network learning that handles heterogeneous edge devices with latency constraints, achieving accuracy improvements with minimal overhead.
MOSAIC is a novel framework that uses a frozen LLM to generate semantic embeddings and hierarchical prediction prompts for knowledge tracing, achieving state-of-the-art results on multiple benchmarks.
Jim Fan announced that the team launched ENPIRE, bringing AutoResearch into the physical world for the first time, equipping 8 Codex agents with robots, GPUs, and tokens, enabling them to autonomously learn and collaborate on real hardware to complete tasks.
OpenAI presents a machine teaching approach where a teacher neural network learns to select the most illustrative examples to teach a student network to recognize concepts, producing interpretable results by grounding examples in human-understandable properties rather than arbitrary feature encodings.