1 citations · 1 across the 7 of their papers we have counts for
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Do We Really Need KL Divergence for On-Policy Distillation of Large Language Models?
Wenze Lin, Jiyuan Long, Jiale Zhao +11
Since the advent of knowledge distillation, KL divergence has been the standard loss in distillation. Recently, on-policy distillation (OPD) has emerged as an efficient post-traini…
From Intent to Evidence: A Categorical Approach for Structural Evaluation of Deep Research Agents
Shuoling Liu, Zhiquan Tan, Kun Yi +6
Deep Research Agents (DRAs) aim to answer complex questions by searching the web, checking evidence, and synthesizing conclusions across heterogeneous sources. We introduce a categ…
CN-Buzz2Portfolio: A Chinese-Market Dataset and Benchmark for LLM-Based Macro and Sector Asset Allocation from Daily Trending Financial News
Liyuan Chen, Shilong Li, Jiangpeng Yan +3
Large Language Models (LLMs) are rapidly transitioning from static Natural Language Processing (NLP) tasks including sentiment analysis and event extraction to acting as dynamic de…
Ten Challenging Problems in Federated Foundation Models
Tao Fan, Hanlin Gu, Xuemei Cao +30
Federated Foundation Models (FedFMs) represent a distributed learning paradigm that fuses general competences of foundation models as well as privacy-preserving capabilities of fed…