4 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…