thu-ml / thu-ml/TurboDiffusion
Strategy for applying rCM with Autoregressive Student
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Description
Hi, thanks for your great work!
I saw causal models were on the roadmap.
For rCM, the DMD component can easily be replaced with Causal Forcing or Self Forcing to keep the rollout on manifold. However I was wondering what the planned approach was for adjusting the sCM loss component so the student can be conditioned on its own rollout? Will using a bidirectional teacher and a causal student conditioned on its own generated sequence be stable for sCM?
Is sCM too sensitive to conditioning mismatch, requiring one of these?
- a different method (sCT?)
- a teacher initialized on self-generated sequences (e.g. resampling forcing)
- perhaps for the sCM path the student and teacher don't need a self-generated sequence (teacher forced ground truth for both models)?
- something else?
Appreciate any insights.
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First steps
- Read the whole issue, then the project's contributing guide.
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Research direction
No file, test, or entry point is named. Start by locating the repository's roadmap and the existing rCM, sCM, DMD, Causal Forcing, and Self Forcing implementations, then compare how teacher and student conditioning are handled; done requires an agreed, stable strategy for autoregressive Student Conditioning Matching.
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Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Quiet
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100