Update README.md

README.md

@@ -156,11 +156,11 @@ If you want to finetune a model to predict human preferences (e.g., for NLG eval
 
 1. **Use a sufficiently large model.** With FLAN-T5-xl, you can get 65-85% percent accuracies depending on the subreddit.
 2. **Do in-domain prediction.** Out-of-domain performance will be poor if the subreddits are unrelated (e.g., if you fine-tune on `askculinary` preferences and test on `askcarguys` preferences).
-3. **Preprocess the data**. The total input length should fit under the model's token limit (usually 512 tokens).
+3. **Preprocess the data.** The total input length should fit under the model's token limit (usually 512 tokens).
    Although models like FLAN-T5 use positional embeddings, we found that the loss would not converge if we finetuned it on the entire input.
    To avoid this, truncate the post text (in the `history` field) as much as possible, such that the whole input is under 512 tokens (do not truncate the comment(s) however).
    If this is still over 512 tokens, simply skip the example.
-4. **Train for 1 epoch only**, as the [InstructGPT paper](https://arxiv.org/abs/2203.02155) paper suggests.
+4. **Train for fewer epochs.** The [InstructGPT paper](https://arxiv.org/abs/2203.02155) paper suggests training a reward model for only 1 epoch.
    Since the same comment appears in multiple preferences, it is easy to overfit to the data.
 5. **Training on less data may help.**
    Preferences with a large score ratio (e.g., comment A having 2x the score of comment B) will provide a stronger signal for finetuning the model, so you may only want to consider preferences above a certain `score_ratio`.