6 papers
Agentic ML Exploration (A-MLE) for Ads Ranking
Erwin Gao, Vinodh Kumar Sunkara, Jingyi Guan +36
Modern industrial ads ranking stacks are increasingly bottlenecked not by model capacity or training compute, but by the throughput of human ML iteration - the cycles of research,…
AgentSpec: Speculative Decoding for Batch Inference of LLM Agents
Xin Wang, Ziming Miao, Yi Zhu +4
Large language model (LLM)-based agent applications often incur high response time. Speculative decoding is a promising solution to improve the inference efficiency of LLM agents w…
LoongRL: Reinforcement Learning for Advanced Reasoning over Long Contexts
Siyuan Wang, Gaokai Zhang, Li Lyna Zhang +4
Reasoning over long contexts is essential for large language models. While reinforcement learning (RL) enhances short-context reasoning by inducing "Aha" moments in chain-of-though…
rStar2-Agent: Agentic Reasoning Technical Report
Ning Shang, Yifei Liu, Yi Zhu +12
We introduce rStar2-Agent, a 14B math reasoning model trained with agentic reinforcement learning to achieve frontier-level performance. Beyond current long CoT, the model demonstr…
rStar-Coder: Scaling Competitive Code Reasoning with a Large-Scale Verified Dataset
Yifei Liu, Li Lyna Zhang, Yi Zhu +5
Advancing code reasoning in large language models (LLMs) is fundamentally limited by the scarcity of high-difficulty datasets, especially those with verifiable input-output test ca…
LongRoPE2: Near-Lossless LLM Context Window Scaling
Ning Shang, Li Lyna Zhang, Siyuan Wang +5
LongRoPE2 is a novel approach that extends the effective context window of pre-trained large language models (LLMs) to the target length, while preserving the performance on the or…