6 papers · 1 filter
Lean4AgenticRL: Advancing Agentic RL with Verifiable Multi-level Process Rewards
Ruida Wang, Gerald Friedland, Tong Zhang +1
Enhancing the agentic capabilities of Large Language Models (LLMs) to tackle complex software engineering (SWE) and general terminal-usage problems is a pressing challenge. Current…
MInTRL: Off-policy Intervention can boost On-policy RL
Mingyu Chen, Yefan Tao, Gerald Friedland +2
Reinforcement learning with verifiable rewards is typically performed on-policy, keeping training data close to the current policy but limiting learning to trajectories that the po…
Mitra-v2 Technical Report
Yefan Tao, Xiyuan Zhang, Xinyi Liu +13
We introduce Mitra-v2, a tabular foundation model that delivers state-of-the-art performance on real-world classification and regression problems, from credit-risk scoring and clin…
Cliff: Learning Process Rewards from the First Mistake
Peixuan Han, Runhui Wang, Ketan Ramaneti +3
Reinforcement learning with verifiable rewards (RLVR) has emerged as a powerful paradigm for large language model (LLM) post-training, but its reliance on coarse outcome rewards le…
When Is Noise Response Universal? Tokenization as the Hidden Variable in Language Models
Yefan Tao, Gerald Friedland, Luyang Kong
The performance of textual neural models often degrades when their inputs are corrupted by noise such as typos, OCR errors, or dropped words. We study the degradation rate across n…
Reflection or Re-Generation? Why LLM Revision Fails Where Human Revision Succeeds
Yefan Tao, Gerald Friedland, Madhusudhanan Chandrasekaran +1
Reflection, the ability to revisit and revise prior reasoning, is central to how humans improve their answers. Large language models (LLMs) are increasingly prompted to "reflect,"…