@lu__jasper

Jasper Lu @lu__jasper · 3h
This is notable. DeepSeek, a lab usually first to pioneer novel algorithms and architectures, is saying that at this point, the ROI of improving data quality far exceeds that of working on novel post-training algorithms.

I think this has already been true for some time for non-lab practitioners. If you're doing llm post-training, 80% of your effort should go into looking at your data.

This means:
- Hiring experts to dig through your RL tasks
- Sifting through rollouts and sft data by hand to remove suspicious samples. Make sure all tasks are actually passable.
- Making sure your data is diverse in both difficulty and category.

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5.1. Post-Training Pipeline

In 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 Lab, 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. [highlighted in yellow from here:] 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. [end highlight]

[Quoted tweet]
DeepSeek @deepseek_ai · 13h
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🚀 Introducing DeepSeek-V4.1-Flash: smarter, faster, more efficient.

🔹 Introducing the smallest model in our new architecture family, with ...

deepseekpost-trainingdata qualityrl environmentsdeepseek-v4.1