Text-to-Reward provides a pipeline to train reward models that map text-based task descriptions or feedback into scalar reward values for RL agents. Leveraging transformer-based architectures and fine-tuning on collected human preference data, the framework automatically learns to interpret natural language instructions as reward signals. Users can define arbitrary tasks via text prompts, train the model, and then incorporate the learned reward function into any RL algorithm. This approach eliminates manual reward shaping, boosts sample efficiency, and enables agents to follow complex multi-step instructions in simulated or real-world environments.
Text-to-Reward Core Features
Natural language–conditioned reward modeling
Transformer-based architecture
Training on human preference data
Easy integration with OpenAI Gym
Exportable reward function for any RL algorithm
Text-to-Reward Pro & Cons
The Cons
The Pros
Automates generation of dense reward functions without need for domain knowledge or data
Uses large language models to interpret natural language goals
Supports iterative refinement with human feedback
Achieves comparable or better performance than expert-designed rewards on benchmarks
Enables real-world deployment of policies trained in simulation
Interpretable and free-form reward code generation
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