2 citations · 3 across the 15 of their papers we have counts for
10 papers · 1 filter
Improving Test-Time Scaling with Adaptive Looped Transformers
Yichen You, Tianyu Fu, Aosong Feng +4
Looped transformers have demonstrated promising parameter efficiency by reusing layers for latent computation. Prior studies compare looped and non-looped models at matched paramet…
SimpleOPD: Simple Tokenizer-Agnostic On-Policy Distillation for Long-Context Reasoning
Haonan He, Haodi Lei, Yun Luo +13
On-policy distillation (OPD) offers a promising way to transfer reasoning capabilities from stronger teacher models, but applying it to long-context reasoning teachers and short-co…
Frontis-MA1: Training an AI4AI Model towards Recursive Self-Improvement in Machine Learning Engineering
Junlin Yang, Che Jiang, Yu Fu +21
Recursive self-improvement (RSI) requires AI systems that improve the process of building AI (i.e., AI4AI); machine learning engineering (MLE) offers a concrete, executable testbed…
Draft-OPD: On-Policy Distillation for Speculative Draft Models
Haodi Lei, Yafu Li, Haoran Zhang +8
Speculative decoding accelerates large language model inference by pairing a target model with a lightweight draft model whose proposed tokens are verified in parallel. A common wa…
Teaching Thinking Models to Reason with Tools: A Full-Pipeline Recipe for Tool-Integrated Reasoning
Qianjia Cheng, Yuchen Zhang, Zhilin Wang +9
Tool-integrated reasoning (TIR) offers a direct way to extend thinking models beyond the limits of text-only reasoning. Paradoxically, we observe that tool-enabled evaluation can d…
LFQA-E: Carefully Benchmarking Long-form QA Evaluation
Yuchen Fan, Chen Lin, Xin Zhong +11
Long-Form Question Answering (LFQA) involves generating comprehensive, paragraph-level responses to open-ended questions, which poses a significant challenge for evaluation due to…
EVA-Score: Evaluating Abstractive Long-form Summarization on Informativeness through Extraction and Validation
Yuchen Fan, Yazhe Wan, Xin Zhong +3
Since LLMs emerged, more attention has been paid to abstractive long-form summarization, where longer input sequences indicate more information contained. Nevertheless, the automat…
Intuitive Fine-Tuning: Towards Simplifying Alignment into a Single Process
Ermo Hua, Biqing Qi, Kaiyan Zhang +4
Supervised Fine-Tuning (SFT) and Preference Optimization (PO) are key processes for aligning Language Models (LMs) with human preferences post pre-training. While SFT excels in eff…
CoGenesis: A Framework Collaborating Large and Small Language Models for Secure Context-Aware Instruction Following
Kaiyan Zhang, Jianyu Wang, Ermo Hua +3
With the advancement of language models (LMs), their exposure to private data is increasingly inevitable, and their deployment (especially for smaller ones) on personal devices, su…
CRaSh: Clustering, Removing, and Sharing Enhance Fine-tuning without Full Large Language Model
Kaiyan Zhang, Ning Ding, Biqing Qi +3
Instruction tuning has recently been recognized as an effective way of aligning Large Language Models (LLMs) to enhance their generalization ability across various tasks. However,…