arXiv:2606.09635v1 Announce Type: cross Abstract: Ensuring the reliability of Large Language Models (LLMs) under distribution drift requires inference-time adaptation. While inference-time alignment methods such as Best-of-$N$ and rejection sampling are widely used, they frame the task as a sampling-intensive, reward-guided search, leading to two key limitations: their performance is bounded by the base model's generation quality, and their reliance on imperfect reward models makes them vulnerable to reward hacking. To address these challenges, we introduce Gradient-Guided Reward Optimization
The rapid advancement and widespread deployment of LLMs highlight the urgent need for robust, inference-time alignment to ensure reliability amidst varying data distributions.
Improving LLM reliability and reducing reliance on sampling-intensive methods directly impacts the efficiency and trustworthiness of AI applications, pushing towards more dependable autonomous systems.
This research introduces a novel, gradient-guided approach to reward optimization for LLMs, moving beyond purely sampling-based methods and potentially mitigating reward hacking vulnerabilities.
- · AI developers and researchers
- · Companies deploying large language models
- · SaaS providers utilizing LLMs
- · Developers solely relying on traditional Best-of-N methods
- · Applications vulnerable to reward hacking
Increased reliability and efficiency of large language models in deployed environments.
Faster and more secure integration of LLMs into critical infrastructure and enterprise applications.
Reduced technical debt and increased trust in autonomous AI agentic systems due to enhanced inference-time alignment capabilities.
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Read at arXiv cs.LG