"Search, Verify and Feedback: Towards Next Generation Post-training Paradigm of Foundation Models via Verifier Engineering"

Verifier Engineering replaces human feedback with automated evaluation systems to improve AI models efficiently.

This paper introduces "Verifier Engineering" - a novel post-training paradigm for foundation models that moves beyond traditional feature and data engineering. It leverages automated verifiers to evaluate and enhance model outputs through a systematic search-verify-feedback cycle.

-----

https://arxiv.org/abs/2411.11504

🤔 Original Problem:

→ Current methods like RLHF and data engineering have hit limitations in improving foundation models due to high costs of human annotations and difficulty in providing meaningful guidance.

-----

🔧 Solution in this Paper:

→ Verifier Engineering introduces a three-stage framework: search, verify, and feedback.

→ The search stage identifies high-quality candidate responses using linear or tree search methods.

→ The verify stage employs multiple automated verifiers to evaluate responses across different dimensions.

→ The feedback stage optimizes model behavior through either training-based or inference-based methods.

→ The entire process is formalized as a Goal-Conditioned Markov Decision Process.

-----

💡 Key Insights:

→ Automated verifiers can replace expensive human annotations

→ Combining multiple verifiers leads to more robust evaluation

→ Goal-aware search improves efficiency over random exploration

-----

📊 Results:

→ Framework successfully explains various approaches from RLHF to newer methods like OmegaPRM

→ Demonstrates higher scalability compared to traditional data engineering

→ Shows improved generalization across different tasks