Brainstorms about visualization and post-hoc analysis
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- Dominant language
- Python
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Description
Since RL is hard to debug, I am wondering whether make everything visualizable would be somehow helpful. For example, a most naive approach is:
- Dump all data to disk, including rollout data and training logprob etc
- Currently we already have a "save data for debug replay" feature, which may be combined
- Create a visualizer to visualize the data, e.g. clickable tables to look at each rollout sample, their token-level prob and ref logprob and whatever, and also some plotly charts for statistics etc
EDIT: potential prerequisite: the buffer refactor in https://github.com/THUDM/slime/issues/80
Contributor guide
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 reviewing the existing “save data for debug replay” feature and the buffer refactor tracked in issue #80. The issue does not name files, entry points, tests, or a defined completion criterion; work would first require narrowing the visualization and post-hoc analysis scope.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- plotly, python
- Domain
- data-visualization, machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100