Entity · technique

Gradient-Guided Reward Optimization

techniqueactivegradient-guided-reward-optimization-9c2867b9·1 events·first seen Jun 9, 2026

Aliases: Gradient-Guided Reward Optimization

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More like this (12)

Improving LLM-Generated Process Model Quality Through Reinforcement Learning: The Role of Reward Function Design Scaling Laws for Reward Model Overoptimization Using Reward Uncertainty to Induce Diverse Behaviour in Reinforcement Learning In-Context Reward Adaptation Adaptive Gated Feedback Optimization Entropy-Regularized Reinforcement Learning rule-based reinforcement learning rewards Reinforcement Learning for Code Optimization Gravity-Weighted Direct Preference Optimization SERPO: Self-Evolving Rubric Policy Optimization for Open-Ended Test-Time Reinforcement Learning Knowledge- and Gradient-Guided Reinforcement Learning for Parametrized Action Markov Decision Processes RREDCoT: Segment-Level Reward Redistribution for Reasoning Models

Recent events (1)

5arXiv · cs.CL·Jun 9, 2026·source ↗

GGRO: Gradient-Guided Reward Optimization for inference-time LLM alignment

Researchers introduce Gradient-Guided Reward Optimization (GGRO), an inference-time alignment method that uses gradient signals from a reward model to inject 'nudging tokens' at high-uncertainty decoding steps, rather than relying on sampling-intensive re-ranking approaches like Best-of-N. The method monitors token-level entropy to detect distribution drift and steers generation trajectories directly, claiming improved robustness to reward hacking with minimal computational overhead. Experiments show gains across safety, helpfulness, and reasoning benchmarks compared to standard inference-time alignment baselines.

Inference Economics Alignment and RLHF Best-of-N Sampling Gradient-Guided Reward Optimization