Explores
This AI Paper Explores Long Chain-of-Thought Reasoning: Enhancing Large Language Models with Reinforcement Learning and Supervised Fine-Tuning
Large language models (LLMs) have demonstrated proficiency in solving complex problems across mathematics, scientific research, and software engineering. Chain-of-thought (CoT) prompting is pivotal in guiding models through intermediate reasoning steps before reaching conclusions. Reinforcement learning (RL) is another essential component that enables structured reasoning, allowing models to recognize and correct errors efficiently. Despite these advancements,…
This AI Paper Explores Reinforced Learning and Process Reward Models: Advancing LLM Reasoning with Scalable Data and Test-Time Scaling
Scaling the size of large language models (LLMs) and their training data have now opened up emergent capabilities that allow these models to perform highly structured reasoning, logical deductions, and abstract thought. These are not incremental improvements over previous tools but mark the journey toward reaching Artificial general intelligence (AGI). Training LLMs to reason well…