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[NeurIPS 2023] Reflexion: Language Agents with Verbal Reinforcement Learning
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| 2026-07-31 | 3217 |
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# [NeurIPS 2023] Reflexion: Language Agents with Verbal Reinforcement Learning This repo holds the code, demos, and log files for [Reflexion: Language Agents with Verbal Reinforcement Learning](https://arxiv.org/abs/2303.11366) by Noah Shinn, Federico Cassano, Edward Berman, Ashwin Gopinath, Karthik Narasimhan, Shunyu Yao.   We have released the LeetcodeHardGym [here](https://github.com/GammaTauAI/leetcode-hard-gym) ## To Run: reasoning (HotPotQA) We have provided a set of notebooks to easily run, explore, and interact with the results of the reasoning experiments. Each experiment consists of a random sample of 100 questions from the HotPotQA distractor dataset. Each question in the sample is attempted by an agent with a specific type and reflexion strategy. ### Setup To get started: 1. Clone this repo and move to the HotPotQA directory: ```bash git clone https://github.com/noahshinn/reflexion && cd ./hotpotqa_runs ``` 2. Install the module dependencies into your environment: ```bash pip install -r requirements.txt ``` 3. Set `OPENAI_API_KEY` environment variable to your OpenAI API key: ```bash export OPENAI_API_KEY=<your key> ``` #### Agent Types Agent type is determined by the notebook you choose to run. The available agent types include: - `ReAct` - ReAct Agent - `CoT_context` - CoT Agent given supporting context about the question - `CoT_no_context` - CoT Agent given no supporting context about the question The notebook for each agent type is located in the `./hotpot_runs/notebooks` directory. #### Reflexion Strategies Each notebook allows you to specify the reflexion strategy to be used by the agents. The available reflexion strategies, which are defined in an `Enum`, include: - `ReflexionStrategy.NONE` - The agent is not given any information about its last attempt. - `ReflexionStrategy.LAST_ATTEMPT` - The agent is given its reasoning trace from its last attempt on the question as context. - `ReflexionStrategy.REFLEXION` - The agent is given its self-reflection on the last attempt as context. - `ReflexionStrategy.LAST_ATTEMPT_AND_REFLEXION` - The agent is given both its reasoning trace and self-reflection on the last attempt as context. ### To Run: decision-making (AlfWorld) Clone this repo and move to the AlfWorld directory ```bash git clone https://github.com/noahshinn/reflexion && cd ./alfworld_runs ``` Specify the run parameters in `./run_reflexion.sh`. `num_trials`: number of iterative learning steps `num_envs`: number of task-environment pairs per trial `run_name`: the name for this run `use_memory`: use persisting memory to store self-reflections (turn off to run a baseline run) `is_resume`: use logging directory to resume a previous run `resume_dir`: the logging directory from which to resume the previous run `start_trial_num`: if resume run, then the trial number of which to start Run the trial ```bash ./run_reflexion.sh ``` The logs will be sent to `./root/<run_name>`. ### Another Note Due to the nature of these experiments, it may not be feasible for individual developers to rerun the results as GPT-4 has limited access and significant API charges. All runs from the paper and additional results are logged in `./alfworld_runs/root` for decision-making, `./hotpotqa_runs/root` for reasoning, and `./programming_runs/root` for programming ### Other Notes Check out the original implementation [here](https://github.com/noahshinn/reflexion-draft) Read one of the original blog posts [here](https://nanothoughts.substack.com/p/reflecting-on-reflexion) Check out an [Appl](https://github.com/appl-team/appl) implementation [here](https://github.com/appl-team/reppl/tree/main/reflexion). Check out an interesting type-prediction implementation here: [OpenTau](https://github.com/GammaTauAI/opentau) For all questions, contact [[email protected]]([email protected]) ### Cite ```b
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Shunyu Yao · Princeton University
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:7650198b1cc305e7, topic:llm