Are AI distills getting worse? Community debates 250-sample trend vs. Qwen R1 8B era
Reddit user questions why distills now use only 250 samples and rarely improve base models.
A post on Reddit’s r/LocalLLaMA by user u/Whydoiexist2983 has sparked discussion about the declining quality of AI model distillation. The user laments the flood of 'x250 sample distills' of models like Mythos and GPT-5.6, arguing that almost none of these distillates outperform their base models—a stark contrast to the Qwen R1 8B distill, which genuinely improved upon the original. The core complaint: modern distills rely on tiny sample sets (~250 examples) and lack the rigor or data diversity that made earlier distills valuable.
The post touches on specific models: Qwen-3.6, Qwen-3.5, and Gemma-4 are cited as base models that rarely see meaningful improvement from these small-scale distills. The user suggests that unless the source model is 'magical' enough to benefit from just a few hundred samples, these distills are largely noise. The community response has been mixed, with some defending the utility of lightweight distills for specific tasks or constrained environments, while others agree that the 250-sample approach has become a lazy default. The thread highlights a broader tension in AI: the race to release 'distilled variants' often sacrifices quality for speed and hype.
- The Qwen R1 8B distill is cited as the last example of a distill that actually improved base model quality.
- Modern distills (e.g., Mythos, GPT-5.6 variants) commonly use only ~250 training samples, which the community argues is insufficient for meaningful improvements.
- Models like Qwen-3.6, Qwen-3.5, and Gemma-4 rarely see enhanced performance from these small-scale distillates.
Why It Matters
For professionals deploying AI, this debate signals a need to verify distill quality rather than assuming smaller means better.