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Sample-Efficient Alignment for LLMs: How to Train Language Models with Less Human Feedback

A new paper published on arXiv proposes a sample-efficient method for aligning large language models (LLMs) with human preferences, a critical step in building trustworthy AI systems. Current methods often require significant human feedback, which can be a bottleneck in developing high-performing models. The new technique, called Sample-Efficient Alignment (SEA), leverages insights from bandit theory, a branch of machine learning that focuses on making optimal decisions with limited information.

SEA treats LLM alignment as a contextual dueling bandit problem, where the LLM (the “agent”) interacts with a human (the “environment”) to receive feedback on its generated responses. The goal is to find a policy that maximizes the probability of generating responses preferred by the human. SEA achieves high sample efficiency by incorporating two key features:

1. Online Interaction: SEA interacts with the human in real-time, learning from the latest feedback to improve its policy. This is in contrast to existing methods that often rely on batch learning, where the model is trained on a fixed dataset of human feedback and then evaluated.

2. Active Exploration: SEA strategically selects which responses to show the human, focusing on those that will provide the most information to improve the model’s understanding of human preferences. This is accomplished through a technique called Thompson sampling, where the model samples from a distribution of possible reward functions and then selects responses that maximize the expected reward according to the sampled function.

The authors demonstrate the effectiveness of SEA through extensive experiments, showing that it outperforms existing methods across various model sizes and preference learning algorithms. In particular, SEA achieves significantly better sample efficiency than existing online methods, requiring fewer human judgments to achieve comparable performance.

One interesting aspect of SEA is its use of an epistemic reward model. Instead of relying on a single reward model, SEA maintains a set of plausible reward models, each representing a different possible way the human might evaluate responses. This allows the model to account for uncertainty in human preferences, leading to more robust and efficient learning.

The paper also presents an open-source codebase for online LLM alignment, making it easier for researchers to implement and build upon SEA. This could accelerate future research in this field, leading to more efficient and effective methods for training LLMs.

In summary, the paper’s main contributions are:

SEA represents a significant step forward in sample-efficient LLM alignment. By reducing the reliance on human feedback, it could pave the way for more efficient and scalable development of trustworthy and powerful AI systems. This work has the potential to accelerate the progress towards creating AI systems that can reliably understand and respond to human needs.