Search, Verify and Feedback: Towards Next Generation Post-training Paradigm of Foundation Models via Verifier Engineering (2411.11504v1)

Abstract: The evolution of machine learning has increasingly prioritized the development of powerful models and more scalable supervision signals. However, the emergence of foundation models presents significant challenges in providing effective supervision signals necessary for further enhancing their capabilities. Consequently, there is an urgent need to explore novel supervision signals and technical approaches. In this paper, we propose verifier engineering, a novel post-training paradigm specifically designed for the era of foundation models. The core of verifier engineering involves leveraging a suite of automated verifiers to perform verification tasks and deliver meaningful feedback to foundation models. We systematically categorize the verifier engineering process into three essential stages: search, verify, and feedback, and provide a comprehensive review of state-of-the-art research developments within each stage. We believe that verifier engineering constitutes a fundamental pathway toward achieving Artificial General Intelligence.

• The paper introduces a three-stage framework (search, verify, feedback) that leverages automated verifiers to refine foundation models.
• The paper employs diverse search methods and evaluation metrics to systematically explore and validate model outputs.
• The paper formulates the process as a Goal-Conditioned Markov Decision Process, enhancing scalability and performance toward AGI.

Exploring Next Generation Post-training Paradigm: Verifier Engineering for Foundation Models

The paper "Search, Verify and Feedback: Towards Next Generation Post-training Paradigm of Foundation Models via Verifier Engineering" explores a sophisticated approach called "verifier engineering" aimed at refining foundation models. As contemporary machine learning models, particularly foundation models like LLMs, evolve, the enhancement of these models' capabilities through traditional data-driven strategies encounters substantial limitations. This paper proposes verifier engineering as a comprehensive, systematic methodology to advance model capabilities in the context of AGI.

Overview of Verifier Engineering

Verifier engineering is characterized by a structured framework incorporating three pivotal stages: search, verify, and feedback. This paper presents a conceptual shift from manual annotations and large-scale data construction to an engineering-driven approach using automated verifiers. The purpose is to bridge the gap between the current performance of foundation models and their potential to achieve AGI by providing enhanced supervision signals beyond conventional methods.

The Three-Stage Framework

1. Search: The search phase involves generating candidate responses from model outputs, intending to explore performance boundaries. This method includes diverse search structures like linear and tree searches, enhancing exploration and identifying optimal response paths more efficiently.
2. Verify: This stage deploys automated verifiers to evaluate candidate responses against predefined goals. The verifiers, categorized by their feedback mechanisms (e.g., binary, score, and text-based), verification granularity, and source (program-based or model-based), serve as a proxy for human judgment, providing comprehensive evaluations.
3. Feedback: Feedback mechanisms are integral to refining model performance based on verification results. This stage includes training-based methods, such as imitation learning that employs high-quality data for supervised fine-tuning, and inference-based strategies that adapt outputs based on verifier feedback without altering the model parameters.

Theoretical and Practical Implications

From a theoretical perspective, verifier engineering presents a paradigm poised to significantly bolster model generalization and scalability, moving machine learning closer to AGI. By formalizing the methodology as a Goal-Conditioned Markov Decision Process (GC-MDP), the paper provides a unified perspective for orchestrating these stages within an optimization framework.

Practically, the implications of verifier engineering are profound. By fostering a closed-loop of continuous improvement, foundation models can self-verify and self-correct without extensive human intervention, significantly reducing costs and overhead associated with data collection and annotation. The framework’s emphasis on a systematic, automated process implies its potential to address complex, dynamic tasks beyond the scope of current models.

Future Directions and Challenges

While verifier engineering presents a promising avenue for the enhancement of foundation models, several challenges remain. The design and integration of verifiers must be carefully tailored to various tasks, requiring further research into verifier composition and selection. Additionally, balancing the trade-offs between exploration and exploitation during the search phase is critical to avoiding local optima.

Moreover, achieving comprehensive generalization across diverse queries remains a non-trivial objective. The feedback mechanisms must be further refined to enhance model capabilities universally, reducing the risk of negative transfer and overfitting to specific tasks.

Conclusion

This exploration into verifier engineering provides a nuanced understanding of how foundational models can evolve in sophistication through systematic supervision. By integrating automated verification processes, the framework encourages a more adaptive, scalable approach towards realizing the potential of AGI. As research continues, the strategies and methodologies outlined in this paper could redefine how AI systems learn and adapt, underpinning future developments in the field.