DeepSeek-R1 incentivizes reasoning in LLMs through reinforcement learning
18 hours ago
- #Large Language Models
- #Reinforcement Learning
- #Artificial Intelligence
- General reasoning in AI is a significant challenge, with recent advancements like large language models (LLMs) and chain-of-thought (CoT) prompting showing success but still requiring extensive human-annotated demonstrations.
- The study demonstrates that LLMs' reasoning abilities can be enhanced through pure reinforcement learning (RL), eliminating the need for human-labeled reasoning trajectories.
- The proposed RL framework encourages the emergence of advanced reasoning patterns such as self-reflection, verification, and dynamic strategy adaptation.
- DeepSeek-R1-Zero, trained using RL, shows superior performance in verifiable tasks like mathematics, coding competitions, and STEM fields compared to models trained with supervised learning on human demonstrations.
- DeepSeek-R1 addresses issues like poor readability and language mixing in DeepSeek-R1-Zero by integrating a multistage learning framework that includes rejection sampling, RL, and supervised fine-tuning.
- The models exhibit self-evolutionary behavior, with reasoning strategies improving over time, including reflective reasoning and exploration of alternative solutions.
- Despite advancements, challenges remain, such as reward hacking, token efficiency, language mixing, and sensitivity to prompts.
- The study highlights the potential of RL to unlock higher capabilities in LLMs, paving the way for more autonomous and adaptive models in the future.