3 papers
cs.AI2025
rSIM: Incentivizing Reasoning Capabilities of LLMs via Reinforced Strategy Injection
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…
cs.CL2025
Guided by Gut: Efficient Test-Time Scaling with Reinforced Intrinsic Confidence
Amirhosein Ghasemabadi, Keith G. Mills, Baochun Li +1
Test-Time Scaling (TTS) methods for enhancing Large Language Model (LLM) reasoning often incur substantial computational costs, primarily due to extensive reliance on external Proc…
cs.CL2025
Boosting of Thoughts: Trial-and-Error Problem Solving with Large Language Models
Sijia Chen, Baochun Li, Di Niu
The reasoning performance of Large Language Models (LLMs) on a wide range of problems critically relies on chain-of-thought prompting, which involves providing a few chain of thoug…