29 citations · 60 across the 28 of their papers we have counts for
7 papers · 1 filter
Is Next-Chunk Reasoning RL Really Better than SFT? Revisiting Training Strategies under no-CoT Data
Yinhao Tang, Youqing Fang, Yanan Sun +8
Recent work proposes next-chunk reasoning RL for leveraging no-CoT data---corpora such as worked solutions and textbook derivations that contain reasoning-rich content but lack exp…
Intern-S2-Mobius: Foundation Model with Decoupled Knowledge and Reasoning
Kai Chen, Jifeng Ding, Ning Ding +44
We introduce Mobius-v0, an architecture that comprises a globally shared Memory (FFN) that stores knowledge vectors and multiple Reasoners (Self-Attn) that iteratively achieve comp…
ThoughtFold: Folding Reasoning Chains via Introspective Preference Learning
Ziyan Liu, Xueda Shen, Yuzhe Gu +7
Large Reasoning Models (LRMs) have achieved remarkable progress thanks to Reinforcement Learning with Verifiable Rewards (RLVR) on Chain-of-Thoughts (CoTs). However, since long CoT…
Achieving Olympiad-Level Geometry Large Language Model Agent via Complexity Boosting Reinforcement Learning
Haiteng Zhao, Junhao Shen, Yiming Zhang +7
Large language model (LLM) agents exhibit strong mathematical problem-solving abilities and can even solve International Mathematical Olympiad (IMO) level problems with the assista…
RIG: Synergizing Reasoning and Imagination in End-to-End Generalist Policy
Zhonghan Zhao, Wenwei Zhang, Haian Huang +4
Reasoning before action and imagining potential outcomes (i.e., world models) are essential for embodied agents operating in complex open-world environments. Yet, prior work either…
Are Your LLMs Capable of Stable Reasoning?
Junnan Liu, Hongwei Liu, Linchen Xiao +6
The rapid advancement of large language models (LLMs) has shown remarkable progress in complex reasoning tasks. However, a significant disparity exists between benchmark performanc…