Reward-Zero: Language Embedding Driven Implicit Reward Mechanisms for Reinforcement Learning

• URL: http://arxiv.org/abs/2603.09331v1
• Date: Tue, 10 Mar 2026 08:07:49 GMT
• Title: Reward-Zero: Language Embedding Driven Implicit Reward Mechanisms for Reinforcement Learning
• Abstract summary: We introduce Reward-Zero, a general-purpose implicit reward mechanism.<n>It transforms natural-language task descriptions into dense, semantically grounded progress signals for reinforcement learning.<n>By comparing the embedding of a task specification with embeddings derived from an agent's interaction experience, Reward-Zero produces a continuous, semantically aligned sense-of-completion signal.
• Score: 17.337313113918043
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce Reward-Zero, a general-purpose implicit reward mechanism that transforms natural-language task descriptions into dense, semantically grounded progress signals for reinforcement learning (RL). Reward-Zero serves as a simple yet sophisticated universal reward function that leverages language embeddings for efficient RL training. By comparing the embedding of a task specification with embeddings derived from an agent's interaction experience, Reward-Zero produces a continuous, semantically aligned sense-of-completion signal. This reward supplements sparse or delayed environmental feedback without requiring task-specific engineering. When integrated into standard RL frameworks, it accelerates exploration, stabilizes training, and enhances generalization across diverse tasks. Empirically, agents trained with Reward-Zero converge faster and achieve higher final success rates than conventional methods such as PPO with common reward-shaping baselines, successfully solving tasks that hand-designed rewards could not in some complex tasks. In addition, we develop a mini benchmark for the evaluation of completion sense during task execution via language embeddings. These results highlight the promise of language-driven implicit reward functions as a practical path toward more sample-efficient, generalizable, and scalable RL for embodied agents. Code will be released after peer review.

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