DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
What this work does
Separates a correctness-reward RL-only reasoning experiment from a practical multi-stage pipeline combining cold-start supervision, RL, rejection sampling and distillation.
Connection to discovery
A baseline for verifiable-outcome optimization and for distinguishing successful-answer reinforcement from learning how to acquire unknown evidence.
Evidence
The consulted training equations, reward definitions and learning curves show which supervision is removed in R1-Zero and which is reintroduced for R1.
Scope and limitations
Known-answer math and executable code provide reward functions that open-ended science may lack. Reasoning gains do not establish novelty, causal identification or experimental competence.
THE RESEARCH CONVERSATION
Discussion & reactions
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