Reward Modeling from Human Feedback Improves Controllability in Large Generative Models
DOI:
https://doi.org/10.54097/z5t42855Keywords:
Reward modeling, reinforcement learning from human feedback, large language models, controllability, preference learning, policy optimization, generative AI alignmentAbstract
Reward modeling from human feedback has emerged as a pivotal technique for directing the behavioral tendencies of large generative models toward outputs that reflect human intentions, value alignment, and task-specific constraints. This paper examines the mechanisms through which reward modeling, embedded within reinforcement learning from human feedback (RLHF) frameworks, enables systematic and fine-grained controllability in large language models (LLMs) and other large-scale generative architectures. We present a multi-stage experimental pipeline encompassing preference dataset construction, reward model training across multiple capacity levels, and policy optimization via proximal policy optimization (PPO) to investigate how reward signal fidelity influences downstream generation controllability. Evaluation dimensions include instruction-following fidelity, toxicity suppression, and stylistic consistency, assessed through both automated metrics and human preference ratings. Results demonstrate that RLHF-trained models substantially outperform supervised fine-tuning (SFT) baselines across all evaluation dimensions, with reward model capacity and preference data diversity emerging as primary determinants of controllability generalization. These findings yield practical guidance for constructing robust alignment pipelines in safety-sensitive and user-facing generative AI deployments.
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