gym-llm is an open-source Python library that integrates large language models with OpenAI Gym interfaces. It provides text-based environments, customizable reward functions, and standard RL loops for training, evaluating, and fine-tuning LLM agents. By leveraging familiar Gym APIs, researchers and developers can benchmark language agents, compare model performance, and iterate on environment design with ease.
AI Platforms & Frameworks
Tool Libraries
#Large Language Models
#text-based environments
#OpenAI Gym
#custom prompt templates
#reinforcement learning
#decision-making agents
#conversational AI
#evaluation metrics
#standardized benchmarks
#reproducible experiments
#task customization
#LLM testing
#agent training
#interactive prompts
#AI Research
#dialogue management
#complex problem solving
#coding environments
#experimental framework
...
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gym-llm
gym-llm is an open-source Python library that integrates large language models with OpenAI Gym interfaces. It provides text-based environments, customizable reward functions, and standard RL loops for training, evaluating, and fine-tuning LLM agents. By leveraging familiar Gym APIs, researchers and developers can benchmark language agents, compare model performance, and iterate on environment design with ease.
AI Platforms & Frameworks
Tool Libraries
#Large Language Models
#text-based environments
#OpenAI Gym
#custom prompt templates
#reinforcement learning
#decision-making agents
#conversational AI
#evaluation metrics
#standardized benchmarks
#reproducible experiments
#task customization
#LLM testing
#agent training
#interactive prompts
#AI Research
#dialogue management
#complex problem solving
#coding environments
#experimental framework
...
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What is gym-llm?
gym-llm extends the OpenAI Gym ecosystem to large language models by defining text-based environments where LLM agents interact through prompts and actions. Each environment follows Gym’s step, reset, and render conventions, emitting observations as text and accepting model-generated responses as actions. Developers can craft custom tasks by specifying prompt templates, reward calculations, and termination conditions, enabling sophisticated decision-making and conversational benchmarks. Integration with popular RL libraries, logging tools, and configurable evaluation metrics facilitates end-to-end experimentation. Whether assessing an LLM’s ability to solve puzzles, manage dialogues, or navigate structured tasks, gym-llm provides a standardized, reproducible framework for research and development of advanced language agents.
Who will use gym-llm?
- AI researchers
- Reinforcement learning practitioners
- LLM developers
- Academic educators
How to use the gym-llm?
- Step1: pip install gym-llm
- Step2: import gym and register a gym-llm environment
- Step3: configure your LLM or RL agent policy
- Step4: run the training loop using env.step(), env.reset()
- Step5: evaluate agent performance and tune reward or prompts
Platform
- mac
- windows
- linux
gym-llm's Core Features & Benefits
The Core Features
- Gym-compatible environments for text-based tasks
- Customizable prompt templates and reward functions
- Standard step/reset/render API for LLM actions
- Integration with RL libraries and loggers
- Configurable evaluation metrics and benchmarks
The Benefits
- Standardized benchmarking of language agents
- Reproducible research workflows
- Easy customization of tasks and rewards
- Seamless integration with existing RL tools
- Accelerates development of conversational and decision-making agents
gym-llm's Main Use Cases & Applications
- Evaluating LLMs on text-based game puzzles
- Benchmarking conversational policies
- Fine-tuning LLMs in decision-making tasks
- Teaching RL concepts in NLP courses
FAQs of gym-llm
gym-llm Company Information
- rsanchezmo
gym-llm Reviews
5/5
gym-llm's Main Competitors and alternatives?
- LangChain
- AgentBench
- OpenAI Gym
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