2 citations · 4 across the 6 of their papers we have counts for
4 papers · 1 filter
APEX: Speculate smarter, not deeper
Manvi Jha, Zach Zhang, Zhichao Xu +3
Speculative decoding reduces large language model inference latency by drafting multiple tokens before target-model verification, but its effectiveness depends on both the proposal…
From Self-Distillation to Self-Practice: Privileged Information for Multi-Turn Agents
Xingyu Su, Abhishek Kumar, Qing Ping +5
On-policy self-distillation (OPSD) has become a popular recipe for post-training LLM agents. It supervises the agent model at the token level with a stronger teacher view of the sa…
PERSONAJUDGE: Simulating Individual Human Preference Judgments with Evaluator-Specific Demonstration Data
Zeyu He, Xuan Qi, Subramanian Chidambaram +4
Large language models increasingly serve as judges in AI evaluation, but current approaches rely on consensus preferences that ignore individual evaluator variation. We propose a n…
An Empirical Study of Automating Agent Evaluation
Kang Zhou, Sangmin Woo, Haibo Ding +15
Agent evaluation requires assessing complex multi-step behaviors involving tool use and intermediate reasoning, making it costly and expertise-intensive. A natural question arises:…