Training with PPO

1. Training with PPO

This video focuses on training a Large Language Model using Proximal Policy Optimization, or PPO, and human feedback. We'll explore the components of a typical PPO training loop and how to use it to fine-tune an LLM.

2. Fine-Tuning with reinforcement learning

At this stage in the RLHF process we have a trained language model that generates text, and a reward model that evaluates text output.

3. Fine-Tuning with reinforcement learning

The next step is to apply reinforcement learning to fine-tune the language model to align more closely with human preferences, guided by the feedback from the reward model.

4. Fine-Tuning a Language Model with PPO

This time, let's say we want to fine-tune a model so that it finishes sentences in the style of rock songs. We pass as a prompt the beginning of a sentence, saying: 'we're halfway',

5. Fine-Tuning a Language Model with PPO

and it generates a response to finish the sentence.

6. Fine-Tuning a Language Model with PPO

Then, the query and response are evaluated by the reward model, resulting in a score value. The score can be on any scale, depending on the data. In this case, it's on a scale of 1 to 3, with 3 being "good". We get a good score as the model completed the sentence with words from a rock song. The score is passed back to the policy model, which will adjust its behavior based on the score value, to complete the training loop.

7. Fine-Tuning a Language Model with PPO

One common policy used here is Proximal Policy Optimization, or PPO. This policy works by staying within a safe range, defined by how much the new policy model differs from the old one. PPO is especially useful in tasks involving human feedback, as it enables incremental improvements to the model's performance. This gradual and controlled way of incorporating feedback is particularly valuable in RLHF. Feedback can vary, and without careful optimization, the model could overfit to the feedback. For example, when giving feedback to give polite responses, the model may start generating overly formal responses that sound too repetitive.

8. Implementing PPOTrainer with TRL

Let's start implementing the PPOTrainer using the TRL library. We first initialize the PPOConfig dataclass, which sets all the hyperparameters and settings for the PPO algorithm and trainer. Next, we initialize the PPOTrainer with the model we want to train. The AutoModelForCausalLMWithValueHead class extends the standard language model by adding a value head to estimate rewards. Now, we're ready to initialize the PPOTrainer using the defined configuration, datasets, and model.

9. Starting the training loop

Here, within each epoch, we iterate through each batch of data with the ppo_trainer.dataloader, using tqdm to track progress. We generate responses from the language model using ppo_trainer.generate and decode these responses into readable text. Next, we combine each query with its response to form a list of texts. These are evaluated by the reward_model to compute reward scores. We then perform a PPO optimization step with the generated responses and computed rewards to update the model, and we can log the results at each iteration using 'log_stats'. This loop iterates over the data, generates responses, evaluates them, and updates the model.

10. Let's practice!

Now that we've covered the basics, let's put our knowledge to work and practice fine-tuning a model!

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