Leveraging Suboptimal, On-Policy Data for Effective Preference Fine-Tuning of Large Language Models

Core Concepts

Approaches that use on-policy sampling or attempt to push down the likelihood on certain responses (i.e., employ a "negative gradient") generally outperform offline and maximum likelihood objectives for preference fine-tuning of large language models.

Abstract

The paper investigates different approaches for fine-tuning large language models (LLMs) using preference data, with the goal of aligning the model's responses with human preferences. The authors analyze a range of fine-tuning methods, including supervised learning, on-policy reinforcement learning (RL), and contrastive learning, and study their performance under various conditions. The key findings are: On-policy sampling generally improves performance and efficiency, especially when the peak of the reward function appears in less likely regions of the reference policy. Some degree of on-policy sample reuse can also reduce the dependency on on-policy sampling, but excessive reuse can hurt exploration. Approaches that use on-policy sampling or attempt to push down the likelihood on certain responses (i.e., employ a "negative gradient") outperform offline and maximum likelihood objectives. This is because these "mode-seeking" objectives can effectively relocate probability mass across bins of the categorical distribution, unlike "mode-covering" maximum likelihood approaches. The performance of different fine-tuning methods is tied to the geometric alignment between the ground-truth reward function and the reference policy, as well as the coverage of the preference data relative to the reference policy. When the peak of the reward function lies in high-likelihood regions of the reference policy, offline supervised methods can work well without the need for on-policy sampling or negative gradients. The authors provide actionable insights for practitioners on choosing the appropriate fine-tuning approach based on the characteristics of the problem and the preference data.

Stats

"When the peak of the reward function lies in the less likely regions of the reference policy, on-policy sampling is generally beneficial." "An explicit negative gradient approach (e.g., via RL objectives or via contrastive objectives) is beneficial when the preference data is skewed away from the reference policy."

Quotes

"Approaches that use on-policy sampling or attempt to push down the likelihood on certain responses (i.e., employ a 'negative gradient') generally outperform offline and maximum likelihood objectives." "Mode-seeking objectives are able to alter probability mass on specific bins of a categorical distribution at a fast rate compared to maximum likelihood, allowing them to relocate masses across bins more effectively."

Key Insights Distilled From

Preference Fine-Tuning of LLMs Should Leverage Suboptimal, On-Policy Data

by Fahim Tajwar... at arxiv.org 04-23-2024

https://arxiv.org/pdf/2404.14367.pdf

Deeper Inquiries

How can the insights from this paper be extended to fine-tuning LLMs for other types of objectives beyond just preference alignment, such as factual correctness or task-specific performance?

The insights from this paper can be extended to fine-tuning LLMs for objectives beyond preference alignment by considering the underlying principles of on-policy sampling and negative gradient utilization. When fine-tuning LLMs for objectives like factual correctness or task-specific performance, the same concepts can be applied to optimize the model's responses towards the desired outcomes. For factual correctness, the model can be fine-tuned using on-policy sampling to focus on generating responses that are factually accurate. By leveraging a negative gradient approach, the model can be trained to minimize the likelihood of incorrect or misleading responses, thereby improving the overall factual correctness of the generated text. Similarly, for task-specific performance, on-policy sampling can be used to guide the model towards generating responses that are more aligned with the task requirements. The negative gradient can help in penalizing responses that deviate from the task-specific objectives, leading to better performance in task completion. Overall, the insights from this paper highlight the importance of on-policy sampling and negative gradient utilization in guiding the fine-tuning process towards specific objectives, which can be applied to a wide range of tasks beyond just preference alignment.

What are the potential downsides or risks of the mode-seeking behavior exhibited by on-policy and contrastive fine-tuning methods, and how can they be mitigated?

The mode-seeking behavior exhibited by on-policy and contrastive fine-tuning methods can have potential downsides or risks, such as the model getting stuck in local optima or overfitting to specific modes in the data distribution. These risks can lead to reduced generalization and limited diversity in the generated responses. To mitigate these risks, several strategies can be employed: Regularization Techniques: Introducing regularization techniques such as dropout or weight decay can help prevent the model from overfitting to specific modes in the data distribution. Regularization encourages the model to explore a wider range of responses. Diverse Training Data: Ensuring that the training data is diverse and representative of the entire data distribution can help mitigate the risk of the model focusing too much on specific modes. Augmenting the training data with variations and edge cases can promote diversity in the model's responses. Exploration Strategies: Incorporating exploration strategies during training, such as epsilon-greedy policies or curriculum learning, can encourage the model to explore different modes in the data distribution and prevent it from getting stuck in local optima. Enforcing Constraints: Setting constraints on the model's output distribution can help maintain diversity in the generated responses. For example, imposing constraints on the entropy of the output distribution can prevent the model from becoming too confident in specific modes. By implementing these strategies, the risks associated with mode-seeking behavior in on-policy and contrastive fine-tuning methods can be mitigated, leading to more robust and diverse model performance.

How can the findings from this paper inform the design of preference data collection mechanisms to maximize the effectiveness of subsequent fine-tuning?

The findings from this paper can provide valuable insights for designing preference data collection mechanisms to enhance the effectiveness of subsequent fine-tuning processes. Here are some ways in which these findings can inform the design of preference data collection: Balanced Data Coverage: The paper emphasizes the importance of data coverage in relation to the reference policy. Preference data should be collected in a way that ensures a balanced representation of responses across different regions of the reference policy distribution. This balanced coverage can help in training models that generalize well across various scenarios. On-Policy Sampling: The effectiveness of on-policy sampling in fine-tuning processes suggests that preference data collection mechanisms should prioritize collecting responses that are more aligned with the current policy. This can be achieved by actively involving the model in the data generation process to ensure that the collected data reflects the model's current behavior. Negative Gradient Labeling: Incorporating negative gradient labeling during preference data collection can help in providing explicit feedback on undesirable responses. By labeling responses that the model should avoid, the subsequent fine-tuning process can be guided towards learning more optimal policies. Adaptive Sampling Strategies: Based on the geometric relationships between the reward function, reference policy, and preference data, adaptive sampling strategies can be employed during data collection. These strategies can dynamically adjust the sampling process to focus on regions where the model needs more improvement. By incorporating these considerations into the design of preference data collection mechanisms, the effectiveness of subsequent fine-tuning processes can be maximized, leading to improved model performance and alignment with desired objectives.