1 citations · 2 across the 11 of their papers we have counts for
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ScalePRM: Training Process Reward Models by Scaling Verification Compute Without Ground Truth
Salman Rahman, Sruthi Gorantla, Arpit Gupta +3
Training process reward models (PRMs) requires step-level correctness labels, obtained either through expensive human annotation or by relying on ground-truth answers, limiting the…
SABER: Small Actions, Big Errors -- Safeguarding Mutating Steps in LLM Agents
Alejandro Cuadron, Pengfei Yu, Yang Liu +1
Despite rapid progress in LLM agents, performance on long-horizon, tool-using tasks remains fragile. To better understand this fragility, we ask a simple question: \emph{do all act…
MM-OPERA: Benchmarking Open-ended Association Reasoning for Large Vision-Language Models
Zimeng Huang, Jinxin Ke, Xiaoxuan Fan +9
Large Vision-Language Models (LVLMs) have exhibited remarkable progress. However, deficiencies remain compared to human intelligence, such as hallucination and shallow pattern matc…
KodCode: A Diverse, Challenging, and Verifiable Synthetic Dataset for Coding
Zhangchen Xu, Yang Liu, Yueqin Yin +2
We introduce KodCode, a synthetic dataset that addresses the persistent challenge of acquiring high-quality, verifiable training data across diverse difficulties and domains for tr…