1 citations · 2 across the 9 of their papers we have counts for
4 papers · 1 filter
PivotOPD: Learning to Recover from Pivotal Mistakes in Multi-Turn Agents
Yinghui He, Yapei Chang, Khushi Bhardwaj +4
On-policy distillation (OPD) is a promising approach for training language agents, providing dense teacher supervision on student-generated trajectories. However, in multi-turn int…
Coresets Before Score Sets: Evaluation-Unsupervised Prompt Subset Selection for LLM Benchmarks
Jihan Yao, Gantavya Bhatt, Arnav Das +16
We study LLM benchmark coreset selection: selecting a small subset of prompts over multiple benchmarks whose induced model scores and rankings approximate those obtained from the f…
PivotRL: High Accuracy Agentic Post-Training at Low Compute Cost
Junkeun Yi, Damon Mosk-Aoyama, Baihe Huang +9
Post-training for long-horizon agentic tasks has a tension between compute efficiency and generalization. While supervised fine-tuning (SFT) is compute efficient, it often suffers…
Human Behavioral Benchmarking: Numeric Magnitude Comparison Effects in Large Language Models
Raj Sanjay Shah, Vijay Marupudi, Reba Koenen +2
Large Language Models (LLMs) do not differentially represent numbers, which are pervasive in text. In contrast, neuroscience research has identified distinct neural representations…