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Amirhosein Ghasemabadi · Keith G. Mills · Baochun Li · Di Niu
Test-Time Scaling (TTS) methods for enhancing Large Language Model (LLM) reasoning often incur substantial inference costs, due to reliance on long chain-of-thought (CoT) generation, self-consistency sampling methods, or searching under Process Reward Models (PRMs). This paper introduces Guided b...
Sijia Chen · Baochun Li · Di Niu
Large language models (LLMs) are post-trained through reinforcement learning (RL) to evolve into Reasoning Language Models (RLMs), where the hallmark of this advanced reasoning is “aha” moments when they start to perform strategies , such as self-reflection and deep thinking, within chain of thou...
Jiuding Yang · Shengyao Lu · Hongxuan Liu · Shayan Shirahmad Gale Bagi · Zahra Fazel · Tomasz Czajkowski · Di Niu
Large language models (LLMs) have achieved remarkable progress in automatic code generation, yet their ability to produce high-performance code remains limited, despite its importance in real-world software systems. We argue that this limitation stems not only from data scarcity, but more fundame...
Haoyu Chen · Xirui Shi · Yuyao Wang · Jerry Chen · Di Niu