Improving Language Model Alignment through Interpretable Reward Engineering

Core Concepts

Designing interpretable reward functions with features like response length, relevance, and consistency can effectively replicate the ground truth reward signal and improve language model alignment.

Abstract

The paper investigates the role of proxy rewards learned from human feedback in aligning large language models (LLMs) through "reverse reward engineering". The key findings are:

Generating responses that are sufficiently lengthy yet relevant and faithful for open-ended queries, while ensuring consistency in responses to closed-ended queries, is crucial for imitating the ground truth reward signal.

Solely optimizing for lengthy responses leads to severe overoptimization and drops in the ground truth reward. Incorporating relevance and differentiating rewards based on query type (open-ended vs. closed-ended) helps mitigate this issue.

The reverse-engineered white-box reward function, which considers response length, relevance, and consistency, often outperforms strong open-source reward models in alignment benchmarks. It also generally works well across different LLM backbones, demonstrating its potential as a simple but robust baseline.

The reward branching and relevance features contribute to minimizing the "alignment tax" - the phenomenon where improved preference comes at the cost of degraded performance on other NLP tasks.

Stats

Longer responses do not necessarily lead to higher ground truth reward scores.
Ensuring relevance of responses to the given query is crucial for maximizing the ground truth reward.
Differentiating rewards based on query type (open-ended vs. closed-ended) helps improve preference while maintaining consistency.

Quotes

"Solely optimizing towards lengthy responses, i.e., LI as a proxy reward, fails to monotonically increase the gold reward signal, contrary to recent findings in Singhal et al. (2023)."
"Considering relevance along with the features reliably increases the gold reward, indicating the success of reverse engineering."
"Reward branching according to whether the query requires open-ended responses makes a meaningful difference, especially for CE type queries."

Key Insights Distilled From

Rethinking the Role of Proxy Rewards in Language Model Alignment

by Sungdong Kim... at arxiv.org 04-30-2024

https://arxiv.org/pdf/2402.03469.pdf

Deeper Inquiries

How can the proposed reward engineering approach be extended to handle more complex and diverse types of queries beyond open-ended and closed-ended

The proposed reward engineering approach can be extended to handle more complex and diverse types of queries by incorporating a more sophisticated feature set that captures the nuances of different query types. For instance, the reward function could include features related to the complexity of the query, the specificity of the information requested, the context of the query, and the desired tone or style of the response. By designing a reward function that considers a broader range of query characteristics, the model can learn to generate responses that are tailored to the specific requirements of each query type. Additionally, incorporating multi-task learning techniques could enable the model to simultaneously optimize for various aspects of the response, such as relevance, coherence, and informativeness, across different query types.

What are the potential limitations of using a large pre-trained reward model (e.g., StarlingRM-34B) as the ground truth, and how can the authors validate the ground truth reward signal more rigorously

Using a large pre-trained reward model like StarlingRM-34B as the ground truth may introduce potential limitations due to the inherent biases and limitations of the model itself. One limitation is the risk of amplifying any biases present in the pre-trained model, which could lead to skewed or inaccurate reward signals. To validate the ground truth reward signal more rigorously, the authors could consider conducting human evaluations to compare the model-generated responses with human-generated responses. This would provide a more reliable benchmark for assessing the quality of the responses and the alignment with human values. Additionally, leveraging multiple diverse human annotators to evaluate the responses can help mitigate individual biases and provide a more comprehensive assessment of the model's performance.

How can the insights from this work be applied to improve the human feedback collection process and mitigate biases in the feedback data

The insights from this work can be applied to improve the human feedback collection process by emphasizing the importance of diverse and representative feedback data. To mitigate biases in the feedback data, the authors could implement strategies such as randomizing the selection of feedback providers, ensuring demographic diversity among the annotators, and incorporating mechanisms for quality control and validation of the feedback. Additionally, the authors could explore the use of adversarial training techniques to detect and correct for biases in the feedback data. By enhancing the robustness and diversity of the human feedback dataset, the model can learn from a more comprehensive and unbiased set of examples, leading to improved alignment with human values.