Jasper Lu(@lu__jasper):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.

2026.09.10 16:08

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.