1 citations · 2 across the 7 of their papers we have counts for
16 papers
Autodata: An agentic data scientist to create high quality synthetic data
Ilia Kulikov, Chenxi Whitehouse, Tianhao Wu +12
We introduce Autodata, a general method that enables AI agents to act as data scientists who build high quality training and evaluation data. We show how to train (meta-optimize) s…
Reasoning over mathematical objects: on-policy reward modeling and test time aggregation
Pranjal Aggarwal, Marjan Ghazvininejad, Seungone Kim +18
The ability to precisely derive mathematical objects is a core requirement for downstream STEM applications, including mathematics, physics, and chemistry, where reasoning must cul…
SPICE: Self-Play In Corpus Environments Improves Reasoning
Bo Liu, Chuanyang Jin, Seungone Kim +7
Self-improving systems require environmental interaction for continuous adaptation. We introduce SPICE (Self-Play In Corpus Environments), a reinforcement learning framework where…
Hybrid Reinforcement: When Reward Is Sparse, It's Better to Be Dense
Leitian Tao, Ilia Kulikov, Swarnadeep Saha +5
Post-training for reasoning of large language models (LLMs) increasingly relies on verifiable rewards: deterministic checkers that provide 0-1 correctness signals. While reliable,…
The Majority is not always right: RL training for solution aggregation
Wenting Zhao, Pranjal Aggarwal, Swarnadeep Saha +3
Scaling up test-time compute, by generating multiple independent solutions and selecting or aggregating among them, has become a central paradigm for improving large language model…
OptimalThinkingBench: Evaluating Over and Underthinking in LLMs
Pranjal Aggarwal, Seungone Kim, Jack Lanchantin +4
Thinking LLMs solve complex tasks at the expense of increased compute and overthinking on simpler problems, while non-thinking LLMs are faster and cheaper but underthink on harder…