Text-to-Reward

Text-to-Reward: Open-Source Framework for RL Reward Models

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Text-to-Reward Product Information

What is Text-to-Reward?

Text-to-Reward offers a complete workflow for training reward models that translate text-based task descriptions or feedback into scalar rewards for reinforcement learning agents. By leveraging transformer-based architectures and fine-tuning on human preference datasets, the system automatically learns to interpret natural language instructions as reward signals. Users can define any task through text prompts, train the model, and integrate the resulting reward function into any RL algorithm. This eliminates manual reward shaping, improves sample efficiency, and allows agents to perform complex multi-step instructions in simulated or real environments.

Who uses Text-to-Reward?

• Reinforcement learning researchers
• Machine learning engineers
• Robotics developers
• AI students and academics
• Game AI developers

How to use Text-to-Reward

• Step 1: Install the Text-to-Reward Python package using pip.
• Step 2: Prepare a dataset containing text instructions paired with preference or reward annotations.
• Step 3: Configure and train the reward model using the included training scripts.
• Step 4: Export the trained model and incorporate it into your RL pipeline (such as OpenAI Gym).
• Step 5: Run your RL agent with the learned reward function and assess its performance.

Platform

• macOS
• Windows
• Linux

Text-to-Reward Core Features & Benefits

Core Features

• Natural language–conditioned reward modeling
• Transformer-based architecture
• Training on human preference data
• Seamless integration with OpenAI Gym
• Exportable reward functions compatible with any RL algorithm

Benefits

• Removes the need for manual reward engineering
• Scales to diverse tasks and environments
• Provides interpretable language-driven reward signals
• Enhances sample efficiency
• Allows customizable task definitions through text

Main Use Cases & Applications

• Robotic control using textual task descriptions
• Game-playing agents that follow language-based goals
• Multi-task reinforcement learning with varied instructions
• Human-in-the-loop feedback for policy improvement
• Simulated environment navigation via language commands

Text-to-Reward Pros & Cons

Pros

Automatically generates dense reward functions without requiring domain knowledge or data

Uses large language models to interpret natural language goals

Supports iterative refinement with human feedback

Achieves performance comparable to or exceeding expert-designed rewards on benchmarks

Enables deployment of policies trained in simulation to real-world settings

Generates interpretable, free-form reward code

Text-to-Reward FAQs

What is Text-to-Reward?

Text-to-Reward is a framework that trains reward models from natural language instructions for reinforcement learning agents.

How do I install Text-to-Reward?

Install via pip: pip install text-to-reward and refer to the documentation for setup instructions.

Which environments are supported?

It works with OpenAI Gym by default and can be adapted for custom simulated or real-world environments.

What models does it use?

It employs transformer-based architectures fine-tuned on human preference data to predict reward values.

How do I prepare training data?

Create a dataset of instruction–reward or preference pairs formatted as outlined in the documentation.

Are pre-trained models available?

Currently, no official pre-trained models are provided; users must train models on their own data.

How can I evaluate the learned reward function?

Integrate it into an RL agent and compare performance against manually designed baselines.

What programming languages are supported?

Text-to-Reward is currently available as a Python library.

Can I contribute to the project?

Yes, contributions are welcome through the GitHub repository; please follow the contribution guidelines.

What license covers Text-to-Reward?

The project is released under the MIT License.

Text-to-Reward Company Information

• Text-to-Reward Project
• xlang-ai
• https://xlang.ai/
• @XLangNLP
• README.md

Text-to-Reward Screenshot