Parent-Guided Semantic Reward Model (PGSRM): Embedding-Based Reward Functions for Reinforcement Learning of Transformer Language Models

arXiv — cs.LG•Tuesday, December 9, 2025 at 5:00:00 AM

PositiveArtificial Intelligence

The Parent-Guided Semantic Reward Model (PGSRM) has been introduced as a novel framework for reinforcement learning in transformer language models, utilizing cosine similarity between output embeddings of parent and child models to generate dense semantic rewards without requiring human annotations or additional training. This approach has been tested across five language tasks, demonstrating smoother reward improvements and more stable dynamics compared to traditional binary reward systems.
The development of PGSRM is significant as it offers a lightweight and efficient alternative to existing reinforcement learning methods, particularly in the context of smaller transformer models. By simplifying the reward generation process, PGSRM could enhance the alignment and performance of language models, potentially leading to more effective applications in natural language processing.
This advancement reflects a broader trend in artificial intelligence research towards optimizing reinforcement learning frameworks, as seen in various methodologies aimed at improving model generalizability and performance across diverse tasks. The emphasis on embedding-based rewards and curriculum mechanisms highlights ongoing efforts to refine training processes and address challenges in noisy environments, ultimately striving for more robust AI systems.

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