Codex Degradation Update on Reddit from OpenAI Employee with Full Report (old.reddit.com)

An OpenAI engineer posted an update on Reddit releasing a full report into a recently observed “Codex” degradation — a measurable drop in the code-generation model’s performance compared with earlier baselines. The post confirms the issue is real, summarizes internal investigation findings and corrective steps, and signals transparency about regressions in a widely used developer-facing model. For the AI/ML community this is significant because it underscores how production ML systems can silently drift or regress after updates, directly impacting downstream tools (IDEs, copilots, automation) that rely on consistent model behaviour. Technically, the report frames the problem as a combination of model and pipeline factors: shifts in training/validation data distributions, changes in preprocessing or tokenization, rollout/configuration differences between training and serving, and gaps in regression testing and benchmark coverage. Recommended mitigations include stronger, task-specific benchmark suites, tighter versioning and dataset provenance, continuous evaluation on held-out real-world tasks, and the ability to roll back model variants quickly. The update is a practical reminder that robust monitoring, reproducible training pipelines, and clear SLAs are essential for maintaining trust in deployed generative models.