What Happened
DeepSeek distilled its open‑source model into a GPT‑OSS‑compatible version. The new model kept many of the original’s censorship rules. A short paper and demo showed the distilled model was noticeably more conservative than a vanilla GPT‑OSS baseline. The authors argued that distillation can copy the teacher’s content‑filtering logic into the student.
Why This Matters for Builders
- Unintended Content Filtering: A distilled GPT‑OSS model may block or alter outputs you expect to be free‑form. Downstream steps that assume full text can break.
- Compliance Risks: Unexpected censorship can hide policy violations or flag legitimate content, leading to audit failures in regulated environments.
- Workflow Reliability: Distilled models trade performance for size. Hidden filters can degrade an agent’s decision‑making, especially when it must adapt to new contexts.
- Testing Overhead: Add tests that verify whether the model is filtering content. Create a controlled set of prompts that trigger the censoring logic and ensure your workflow handles altered responses.
FAQ
Q: Can I simply switch to a newer GPT‑OSS release to avoid censorship?
A: Newer releases may not inherit the same filters, but they can still carry content policies from their training data. Always audit the model’s behavior against your use cases.
Q: How do I detect if a distilled model has hidden censorship?
A: Run a suite of prompts that cover your workflow’s topics. Compare the outputs to a reference model. Consistent suppression or redaction indicates potential censorship.
Q: Should I avoid distillation altogether for production workflows?
A: Not necessarily. Distillation cuts inference cost and latency, but it can bring inherited filters. If you distill, add explicit content‑filter checks and fallback logic to your automation pipeline.
Originally published on Automations Cookbook.
