一作ICLR 2026Poster· Applications / Language, Speech and Dialog
SPRIG: Improving Large Language Model Performance by System Prompt Optimization
Lechen Zhang · Tolga Ergen · Lajanugen Logeswaran · Moontae Lee · David Jurgens
多模态OpenReview
首次获取该候选人的完整档案及 StartLux 评估共 20 积分；每位用户单独解锁，之后查看免费。主动刷新 20 积分。
实时查找电子邮箱，费用不超过20积分；未找到邮箱不扣费。
Lechen Zhang · Tolga Ergen · Lajanugen Logeswaran · Moontae Lee · David Jurgens
Lechen Zhang · Yunxiang Zhang · Wei Hu · Lu Wang
Large reasoning models such as DeepSeek-R1 and their distilled variants achieve strong performance on complex reasoning tasks. Yet, distilling these models often demands large-scale data for supervised fine-tuning (SFT), motivating the pursuit of data-efficient training methods. To address this,...
Yunxiang Zhang · Muhammad Khalifa · Lechen Zhang · Xin Liu · Ayoung Lee · Xinliang Frederick Zhang · Farima Fatahi Bayat · Lu Wang
Large reasoning models exhibit long chain-of-thought reasoning with complex strategies such as backtracking and self-verification. Yet, these capabilities typically require resource-intensive post-training. We investigate whether such behaviors can be elicited in large models without any gradient...
Jonathan Ivey · Shivani Kumar · Jiayu Liu · Hua Shen · Sushrita Rakshit · Rohan Raju · Haotian Zhang · Aparna Ananthasubramaniam · Junghwan Kim · Bowen Yi · Dustin Wright · Abraham Israeli · Anders Giovanni Møller · Lechen Zhang · David Jurgens
Building datasets for dialogue tasks is expensive and time-consuming, requiring recruitment, training, and data collection from study participants. In response, much recent work has sought to use large language models (LLMs) to simulate both human-human and human-LLM interactions, as they have be...