Comments (1)
rajcscw
commented on May 9, 2024
- So each run will have some randomness due to the dataset creation (we randomly select val and test samples) due to the large size of the original dataset.
- Additionally, during decoding, there is randomness due to the sampling of tokens (both during epoch 0 and epoch 50)
- Randomness in episode generation of PPO
Also can you tell the exact mean and sd of these runs and the corresponding config? We can double-check from our side too.
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Related Issues (20)
- `train` and `val` splits are not disjoint for IMDB HOT 3
- Resuming from checkpoint is potentially problematic for IMDB since the splits are resampled HOT 1
- In the paper, what is the detail setting of supervised learning? Is SL has additional supervised data?
- [Question] End-to-end example
- Error with Accelerate integration + NLPO HOT 1
- Bug while loading t5 base model HOT 1
- How can I inference data with the model after PPO training?
- Using GPT-2
- 'GPT2Model' object has no attribute 'first_device'
- model.generate.scores returning two scores
- CPU Support Minor Bug
- is multi-dimensional reward supported?
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- Reproducing existing results on NarrativeQA
- NLPO Code Error and Query About gymnasium vs gym Usage
- Pip install error with gym and torch HOT 2
- Do you have any plans to apply the recently published Reinforced Self-Training (ReST)?
- Is PPO really better than SFT (in general)? under the condition of same amount of data HOT 1
- how to stop env parallel multi-process to debug env.step()?
- Upgrade to torch 2.0 HOT 1
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