Comments (5)
I also find that repetition_penalty can cause that issue, so I removed that parameter in the latest version.
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It is the latest version of textrl?
The issue could very likely be due to a significant update in the optimizer. Try adjusting the optimizer to see what effect it has.
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it seems like the function top_k_top_p_filtering
in the forward function of SoftmaxCategoricalHead class results in this error..?
it filters the value by replacing -inf, which causes loss_policy
runs into nan
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I'm sorry to bother but still want to ask,
is it reasonable to put top k top p filter inside the forward function? cause it would change the log probability when training
I suppose we only have to do it before sampling action?
from textrl.
I'm sorry to bother but still want to ask,
is it reasonable to put top k top p filter inside the forward function? cause it would change the log probability when training
I suppose we only have to do it before sampling action?
When we use model, we normally use top k top p sampling, my opinion is to align rl training to the actual usage.
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Related Issues (20)
- Backward compatibility HOT 2
- About the compare_sample HOT 1
- i get error when i use elon example HOT 6
- Text generation models generating repeated/duplicate text/sentences. HOT 3
- Are there any examples for T5 or Bart? Why T5 and bart give the same output before/after training? HOT 2
- Support for AutoModelForSeq2SeqLM HOT 2
- Update interval HOT 3
- Documentation on Methodology HOT 1
- Support for other PFRL Algorithms HOT 2
- Text generation after period/full-stop (".")
- Reward policy agent environment is not training with Finetuned model HOT 1
- Does the package support automatic multi-gpu? HOT 2
- unfreeze_layer_from_past parameter HOT 4
- Problems in the inference process
- Could you give some examples to run the code? HOT 3
- AttributeError: 'MyRLEnv' object has no attribute 'num_envs' HOT 2
- AttributeError: module 'numpy' has no attribute '_no_nep50_warning' HOT 1
- It needs a license HOT 1
- AssertionError HOT 3
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