MechanismArticle
During DeepSeek-R1's RLVR training, the reasoning trace itself was not used as a training signal — the reward was based only on the final answer's correctness and format, since including the trace in training was reported to not help.
Raschka explains that RLVR rewards only the final answer and format, not the intermediate reasoning trace, per the DeepSeek-R1 paper's findings. ✦ AI generated
Sebastian Raschka · Ahead of AI · 2026-07-18 · original ↗
Notably, the reasoning trace itself was not used for training or updating the model. Although they tried to use this intermediate response information for training, the DeepSeek-R1 paper reported that it wasn't helpful for the model training, so it was ultimately not used.
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explains mechanism → The <think></think> tags are purely cosmetic markers for separating the reasoning trace from the final answer; they do not confer or improve reasoning ability, and a model trained without them would likely perform just as well.Sebastian Raschka · Ahead of AIgives example → RLVR trains language models by having them attempt verifiable tasks like math or code, using measured accuracy on those tasks as the reward signal for reinforcement learning updates.Nathan Lambert · Lex Fridman