Fine-tune LLM agents with online reinforcement learning

🔗 Agents for Web Data Extraction   •   🐦 Twitter # LlamaGym "Agents" originated in reinforcement learning, where they learn by interacting with an environment and receiving a reward signal. However, LLM-based agents today do not learn online (i.e. continuously in real time) via reinforcement. OpenAI created [Gym](https://github.com/Farama-Foundation/Gymnasium) to standardize and simplify RL environments, but if you try dropping an LLM-based agent into a Gym environment for training, you'd find it's still quite a bit of code to handle LLM conversation context, episode batches, reward assignment, PPO setup, and more. LlamaGym seeks to simplify fine-tuning LLM agents with RL. Right now, it's a single `Agent` abstract class that handles all the issues mentioned above, letting you quickly iterate and experiment with agent prompting & hyperparameters across any Gym environment. ## Usage Fine-tuning an LLM-based agent to play in a Gym-style environment with RL has never been easier! Once you install LlamaGym... ``` pip install llamagym ``` First, implement 3 abstract methods on the Agent class: ```python from llamagym import Agent class BlackjackAgent(Agent): def get_system_prompt(self) -> str: return "You are an expert blackjack player." def format_observation(self, observation) -> str: return f"Your current total is {observation[0]}" def extract_action(self, response: str): return 0 if "stay" in response else 1 ``` Then, define your base LLM (as you would for any fine-tuning job) and instantiate your agent: ```python model = AutoModelForCausalLMWithValueHead.from_pretrained("Llama-2-7b").to(device) tokenizer = AutoTokenizer.from_pretrained("Llama-2-7b") agent = BlackjackAgent(model, tokenizer, device) ``` Finally, write your RL loop as usual and simply call your agent to act, reward, and terminate: ```python env = gym.make("Blackjack-v1") for episode in trange(5000): observation, info = env.reset() done = False while not done: action = agent.act(observation) # act based on observation observation, reward, terminated, truncated, info = env.step(action) agent.assign_reward(reward) # provide reward to agent done = terminated or truncated train_stats = agent.terminate_episode() # trains if batch is full ``` Some reminders: - above code snippets are mildly simplified above but a fully working example is available in [`examples/blackjack.py`](https://github.com/KhoomeiK/LlamaGym/blob/main/examples/blackjack.py) - getting online RL to converge is notoriously difficult so you'll have to mess with hyperparameters to see improvement - your model may also benefit from a supervised fine-tuning stage on sampled trajectories before running RL (we may add this feature in the future) - our implementation values simplicity so is not as compute efficient as e.g. [Lamorel](https://github.com/flowersteam/lamorel), but easier to start playing around with - LlamaGym is a weekend project and still a WIP, but we love contributions! ## Relevant Work - [Grounding Large Language Models with Online Reinforcement Learning](https://github.com/flowersteam/Grounding_LLMs_with_online_RL) - [Lamorel: Language Models for Reinforcement Learning](https://github.com/flowersteam/lamorel) - [True Knowledge Comes from Practice: Aligning LLMs with Embodied Environments via Reinforcement Learning](https://github.com/WeihaoTan/TWOSOME) ## Citation ``` bibtex @misc{pandey2024llamagym, title = {LlamaGym: Fine-tune LLM agents with Online Reinforcement Learning}, author = {Rohan Pandey}, year = {2024}, howpublished = {GitHub}, url = {https://github.com/KhoomeiK/LlamaGym} } ```