Unverified50% confidenceFactExact time
Open weights mean others can load, run inference on, fine-tune or distill the model, but they cannot directly replicate the distributed strategy, data cleaning pipeline, alignment methods and hardware scheduling experience in the training pipeline.
1
Sources
50%
Confidence
Long-term
Relevance
10/18/2026
First Seen
Sources
DeepSeek Insists on Open Source—Is It Really Handing Ammunition to OpenAI?
bilibili天天速递乐子10/18/2026
Related Entities
Related Claims
UnverifiedThe factors that determine the upper limit of model capabilities are often training compute and data quality, both of which are difficult to share through simple open-sourcing74% similarUnverifiedRestricting training data or fine-tuning directions can only affect a model's default behavior, and cannot prevent technically capable individuals from bypassing restrictions through prompt engineering or model modification.73% similarUnverifiedFor pure inference scenarios, model weights typically only need to be loaded once, and the constraint of data gravity is far less significant than in the training phase.72% similarVerifiedOpen weights differ from fully open source: the former makes model parameters public but does not necessarily disclose training data and complete training code.71% similarUnverifiedOpenAI's research on instruction following and web-based learning systems shows that the training-layer dynamics are far more complex than they appear on the surface, and training data cannot be directly optimized tactically.71% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1059812API
curl https://kongchang.com/api/v1/knowledge/claims/1059812MCP
get_claim(id=1059812)