14 papers
Recovering Wasted Compute in Autoresearch Agents
Au Kwok Chun, Abhigyan Acherjee, Amrutha Rao +4
A slew of recent works develop agents for solving research problems end-to-end, a paradigm increasingly referred to as autoresearch. Such agents have inspired large industry invest…
Understanding Reasoning from Pretraining to Post-Training
Jingyan Shen, Ang Li, Salman Rahman +4
Reinforcement learning (RL) has become central to improving large language models (LLMs) on complex reasoning tasks, yet RL post-training is largely studied in isolation from the p…
End-to-End Context Compression at Scale
Ang Li, Sean McLeish, Haozhe Chen +12
Long-context language model inference is bottlenecked by memory, as the KV cache grows with context length. Recent techniques to compress the KV cache fall short: they either degra…
On Training in Imagination
Nadav Timor, Ravid Shwartz-Ziv, Micah Goldblum +2
State-of-the-art model-based reinforcement learning methods train policies on imagined rollouts. These rollouts are trajectories generated by a learned dynamics model and are score…
LiveMathematicianBench: A Live Benchmark for Mathematician-Level Reasoning with Proof Sketches
Linyang He, Qiyao Yu, Hanze Dong +5
Mathematical reasoning is a hallmark of human intelligence, and whether large language models (LLMs) can meaningfully perform it remains a central question in artificial intelligen…
Privacy-Preserving Mechanisms Enable Cheap Verifiable Inference of LLMs
Arka Pal, Louai Zahran, William Gvozdjak +2
As large language models (LLMs) continue to grow in size, fewer users are able to host and run models locally. This has led to increased use of third-party hosting services. Howeve…