This paper surveys recent replication studies of DeepSeek-R1, focusing on Supervised Fine-Tuning (SFT) and Reinforcement Learning from Verifiable Rewards (RLVR). It details data construction, method design, and training procedures, offering insights and anticipating future research directions for reasoning language models.
The article introduces a new approach to language modeling called test-time scaling, which enhances performance by utilizing additional compute resources during testing. The authors present a method involving a curated dataset and a technique called budget forcing to control compute usage, allowing models to double-check answers and improve reasoning. The approach is demonstrated with the Qwen2.5-32B-Instruct language model, showing significant improvements on competition math questions.
