There is still Alpha to be had in good training data.
Source huggingface.coFrom DeepSeek’s paper:
Post-Training PipelineIn this release, we refrain from introducing novel post-training algorithms. The overall recipe follows the standard paradigm of supervised fine-tuning (SFT) followed by reinforcement learning (RL) and on-policy distillation (OPD) [Gu et al., 2024; Lu and Lall, 2025], without algorithmic modifications beyond well-established practices. Instead, our efforts are concentrated almost entirely on what the model is trained on rather than how it is optimized: we invest in large-scale, automated pipelines for data synthesis and environment construction. Concretely, the pipeline (i) synthesizes diverse, verifiable training tasks together with their reference solutions and reward signals, (ii) procedurally constructs and scales interactive Agent environments in which trajectories can be collected and evaluated at low cost, and (iii) applies rigorous filtering, deduplication, and difficulty calibration to ensure data quality and curriculum balance. We find that, under a fixed and unremarkable optimization procedure, systematic improvements in the scale, diversity, and verifiability of synthesized data and environments account for essentially all of the observed gains. This observation echoes a broader lesson: at the current stage, the marginal return of engineering the data and environment pipeline substantially exceeds that of algorithmic novelty in post-training.
Nearby leaves, connected by subject and form.