Learn what KL divergence is and how it is used in reinforcement learning and fine-tuning techniques to measure and constrain the difference between probability distributions.
Table of Contents
Question
In reinforcement learning, particularly with the Proximal Policy Optimization (PPO) algorithm, what is the role of KL-Divergence? Select all that apply.
A. KL divergence is used to train the reward model by scoring the difference of the new completions from the original human-labeled ones.
B. KL divergence measures the difference between two probability distributions.
C. KL divergence is used to enforce a constraint that limits the extent of LLM weight updates.
D. KL divergence encourages large updates to the LLM weights to increase differences from the original model.
Answer
B. KL divergence measures the difference between two probability distributions.
C. KL divergence is used to enforce a constraint that limits the extent of LLM weight updates.
Explanation
The correct answers are B and C. KL divergence measures the difference between two probability distributions, and it is used to enforce a constraint that limits the extent of LLM weight updates.
B is true because KL divergence is a way to compare differences between two probability distributions p(x) and q(x). It measures how much information is lost when q(x) is used to approximate p(x). It can also be interpreted as the expected excess surprise from using q(x) as a model when the actual distribution is p(x).
C is true because KL divergence is used in the Proximal Policy Optimization (PPO) algorithm to ensure that the new policy is not too far from the old policy. PPO is a reinforcement learning algorithm that trains an agent’s policy to perform well in complex tasks. PPO uses a novel objective function that encourages the agent to improve its policy while staying close to its previous policy. This is achieved by applying a constraint that penalizes the agent if the KL divergence between the new and old policy probabilities exceeds a certain threshold. This constraint ensures that the policy update is not too large and does not harm the agent’s performance.
A is false because KL divergence is not used to train the reward model by scoring the difference of the new completions from the original human-labeled ones. This is a description of a technique called Reinforcement Learning from Human Feedback (RLHF), which trains a reward model directly from human feedback and uses the model as a reward function to optimize an agent’s policy using reinforcement learning (RL). However, RLHF does not use KL divergence to train the reward model; instead, it uses a cross-entropy loss function that measures the difference between the human labels and the model predictions.
D is false because KL divergence does not encourage large updates to the LLM weights to increase differences from the original model. On the contrary, KL divergence discourages large updates to the LLM weights to preserve the original model’s knowledge and skills. This is a technique called Parameter-Efficient Fine-Tuning (PEFT), which enables efficient adaptation of pre-trained language models (PLMs) to various downstream applications without fine-tuning all the model’s parameters. PEFT methods only fine-tune a small number of (extra) model parameters, such as adapters, prefixes, or soft prompts, that are inserted into the original model layers. This reduces the computational and storage costs of fine-tuning, as well as the risk of overfitting or catastrophic forgetting. KL divergence is used as a regularization term in some PEFT methods, such as adapter, to constrain the distance between the original and the adapted model outputs.
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