Tencent / Tencent/digitalhuman
The alignment loss converges very fast
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 361
- Forks
- 52
- PR merge metrics
- No merged PRs in 30d
Description
Hi, I have tried reimplementing the alignment loss recently. The alignment loss seemed to converge very fast. After 200 steps, the alignment loss decreased to 0.02, while the cross entrope loss was 1.0 around. Is that a normal phenomenon?
Look forward to your kind reply, thank you~
Contributor guide
No contributing guide indexed for this repository
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reproducing the reported training behavior and inspect the alignment-loss and cross-entropy calculations used by the training entry point. Compare both loss curves around step 200 and verify whether the reported values are expected; done means documenting the cause and confirming whether a code change is required.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100