Update README.md

README.md

@@ -7,16 +7,20 @@
 ---
 
 # BALROG: Benchmarking Agentic LLM and VLM Reasoning On Games
+
 BALROG is a novel benchmark evaluating agentic LLM and VLM capabilities on long-horizon interactive tasks using reinforcement learning environments. Check out how current models fare on our [leaderboard](https://balrogai.com). You can read more about BALROG in our [paper](https://arxiv.org/abs/2411.13543).
 
 ## Features
+
 - Comprehensive evaluation of agentic abilities
 - Support for both language and vision-language models
 - Integration with popular AI APIs and local deployment
 - Easy integration for custom agents, new environments and new models
 
 ## Installation
+
 We advise using conda for the installation
+
 ```bash
 conda create -n balrog python=3.10 -y
 conda activate balrog
@@ -26,12 +30,15 @@ cd BALROG
 pip install -e .
 balrog-post-install
 ```
+
 On Mac make sure you have `wget` installed for the `balrog-post-install`
 
 ## Docker
+
 We have provided some docker images. Please see the [relevant README](docker/README.md).
 
 ## ⚡️ Evaluate using vLLM locally
+
 We support running LLMs/VLMs locally using [vLLM](https://github.com/vllm-project/vllm). You can spin up a vLLM client and evaluate your agent on BALROG in the following way:
 
 ```bash
@@ -49,43 +56,53 @@ python eval.py \
 ```
 
 On Mac you might have to first export the following to suppress some fork() errors:
+
 ```
 export OBJC_DISABLE_INITIALIZE_FORK_SAFETY=YES
 ```
 
 Check out [vLLM](https://github.com/vllm-project/vllm) for more options on how to serve your models fast and efficiently.
 
-## 🛜 Evaluate using popular APIs
-We support out of the box clients for OpenAI, Anthropic and Google Gemini APIs. First set up your API key:
+## 🛜 Evaluate using API
+
+We support how of the box clients for OpenAI, Anthropic and Google Gemini APIs. If you want to evaluate an agent using one of these APIs, you first have to set up your API key in one of two ways:
+
+You can either directly export it:
 
 ```bash
 export OPENAI_API_KEY=<KEY>
 export ANTHROPIC_API_KEY=<KEY>
 export GEMINI_API_KEY=<KEY>
 ```
 
-Then run the evaluation with:
+Or you can modify the `SECRETS` file, adding your api keys.
+
+You can then run the evaluation with:
 
 ```bash
 python eval.py \
   agent.type=naive \
   agent.max_image_history=0 \
-  eval.num_workers=64 \
+  agent.max_history=16 \
+  eval.num_workers=16 \
   client.client_name=openai \
   client.model_id=gpt-4o-mini-2024-07-18
 ```
 
 ## Documentation
+
 - [Evaluation Guide](https://github.com/balrog-ai/BALROG/blob/main/docs/evaluation.md) - Detailed instructions for various evaluation scenarios
 - [Agent Development](https://github.com/balrog-ai/BALROG/blob/main/docs/agents.md) - Tutorial on creating custom agents
 - [Few Shot Learning](https://github.com/balrog-ai/BALROG/blob/main/docs/few_shot_learning.md) - Instructions on how to run Few Shot Learning
 
 We welcome contributions! Please see our [Contributing Guidelines](https://github.com/balrog-ai/BALROG/blob/main/docs/contribution.md) for details.
 
 ## License
+
 This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
 
 ## Citation
+
 If you use BALROG in any of your work, please cite:
 
 ```