Shafiq Joty's team proposes Procedural Memory Distillation for less forgetful LLM training
PMD is presented as a training-time method that stores lessons from prior model attempts, then uses them to teach the next policy.
By Ryan Merket · Published
Primary source: X
Why it matters
If PMD's claims hold up, it points to a practical path for AI teams to reuse failure traces during training, not just add retrieval at inference time.
Shafiq Joty (@JotyShafiq) introduced Procedural Memory Distillation, or PMD, in a thread on X, framing it as a way to make feedback-based LLM training carry lessons forward instead of treating each attempt as disposable.