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environments/community/humor_generation/README.md

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+# Humor Generation Environment
+
+## Overview
+A reinforcement learning environment for training language models to generate humor in the style of specific comedians and formats. The environment uses a multi-dimensional scoring rubric to evaluate joke quality across relevance, style consistency, creativity, humor effectiveness, virality, and cognitive coherence.
+
+## Features
+- **Multi-Comedian Training**: Supports various comedian styles (Norm Macdonald, John Mulaney, Hasan Minhaj, Dave Chappelle, Ali Wong, Chris Rock)
+- **Format Diversity**: Trains on different humor formats (haiku, one-liner, q/a over SMS)
+- **Comprehensive Scoring**: 6-dimensional evaluation rubric for joke quality assessment
+- **Dataset Generation**: Automated dataset creation using GPT-4o-mini
+- **WandB Integration**: Comprehensive experiment tracking and visualization
+
+## Environment Structure
+- `humor_env.py`: Main environment implementation with scoring logic
+- `generate_humor_dataset.py`: Script for creating training datasets
+- `humor_dataset.jsonl`: Pre-generated dataset with comedian/format combinations
+
+## Scoring Rubric
+The environment evaluates generated jokes across six dimensions (0-3 points each):
+1. **Relevance to Format** (0-2): How well the joke fits the specified format
+2. **Style Consistency** (0-2): Adherence to the target comedian's style
+3. **Creativity** (0-3): Originality and inventiveness of the humor
+4. **Humor Effectiveness** (0-3): How funny and engaging the joke is
+5. **Virality** (0-3): Potential for widespread appeal and sharing
+6. **Cognitive Coherence** (0-3): Logical structure and comprehensibility
+
+## Usage
+
+### Running the Environment
+```bash
+python environments/community/humor_generation/humor_env.py serve
+```
+
+### Generating New Datasets
+```bash
+cd environments/community/humor_generation/
+python generate_humor_dataset.py
+```
+
+## Configuration
+- **Model**: GPT-4o-mini for both generation and evaluation
+- **Group Size**: 2 completions per prompt
+- **Max Tokens**: 2048 for joke generation, 512 for scoring
+- **Evaluation**: LLM-based scoring using detailed rubric prompts
+
+## Requirements
+- OpenAI API key (set as `OPENAI_API_KEY` environment variable)
+- Standard Atropos dependencies
+- WandB account for experiment tracking
+
+## Dataset Format
+Each record contains:
+- `comedian`: Target comedian style
+- `format`: Humor format (haiku, one-liner, q/a over SMS)
+- `question`: Prompt asking for model recommendations and example jokes
+- `response`: GPT-4o-mini generated response with explanations and examples
+
+## Training Applications
+- **Style Transfer**: Learning to mimic specific comedian voices
+- **Format Adaptation**: Generating humor in constrained formats
+- **Quality Assessment**: Training models to evaluate humor effectiveness
+- **Creative Writing**: Developing AI systems for entertainment content creation