Reward-Zero: Language Embedding Driven Implicit Reward Mechanisms for Reinforcement Learning
arXiv:2603.09331v1 Announce Type: new 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
arXiv:2603.09331v1 Announce Type: new 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.
Executive Summary
This article introduces Reward-Zero, a novel implicit reward mechanism that utilizes language embeddings to provide a semantically grounded sense of completion for reinforcement learning (RL). By leveraging task descriptions and agent interaction experiences, Reward-Zero generates a continuous, semantically aligned reward signal that accelerates exploration, stabilizes training, and enhances generalization across diverse tasks. Empirical results demonstrate the effectiveness of Reward-Zero in solving complex tasks that conventional methods struggle with. The article also proposes a mini benchmark for evaluating completion sense during task execution via language embeddings.
Key Points
- ▸ Reward-Zero is a general-purpose implicit reward mechanism for RL that leverages language embeddings.
- ▸ Reward-Zero generates a semantically grounded sense of completion by comparing task descriptions and agent interaction experiences.
- ▸ Empirical results show that Reward-Zero outperforms conventional methods in solving complex tasks.
Merits
Strength in leveraging language embeddings
Reward-Zero's use of language embeddings enables a semantically grounded sense of completion, which is a significant improvement over conventional methods that rely on sparse or delayed environmental feedback.
Improved exploration and training stability
Reward-Zero's continuous and semantically aligned reward signal accelerates exploration and stabilizes training, leading to better convergence and higher final success rates.
Enhanced generalization and scalability
Reward-Zero's ability to generalize across diverse tasks and its potential for scalability make it a promising approach for more sample-efficient and generalizable RL.
Demerits
Lack of task-specific engineering
While Reward-Zero eliminates the need for task-specific engineering, it may require significant computational resources to process language embeddings, which could be a limitation for resource-constrained applications.
Dependence on high-quality language embeddings
Reward-Zero's performance relies heavily on the quality of language embeddings, which can be a challenge in scenarios with limited or noisy training data.
Expert Commentary
The article presents a well-structured and engaging introduction to Reward-Zero, a novel implicit reward mechanism that leverages language embeddings to provide a semantically grounded sense of completion for RL. While the results are promising, it is essential to consider the limitations and challenges associated with Reward-Zero, such as the dependence on high-quality language embeddings and the need for significant computational resources. The article's focus on language-driven implicit reward functions and embodied agents opens up new avenues for research in RL, which could have significant implications for real-world applications. To further validate the results and explore the potential of Reward-Zero, it is recommended to conduct more extensive experiments and investigate the impact of language embeddings on RL performance in various domains.
Recommendations
- ✓ Future research should focus on developing more efficient and scalable methods for generating high-quality language embeddings.
- ✓ The development of task-specific architectures and algorithms that integrate Reward-Zero could lead to even better performance and generalization.