README.md

---
title: Exec Assistant Arena Environment Server
emoji: 📋
colorFrom: gray
colorTo: green
sdk: docker
pinned: false
app_port: 8000
base_path: /web
tags:
  - openenv
---

# Executive Assistant Arena

An [OpenEnv](https://github.com/meta-pytorch/OpenEnv) environment that simulates a personal assistant's morning inbox. The LLM agent must resolve calendar conflicts, draft email replies, infer hidden user preferences, and handle late-breaking schedule changes.

Trained a Qwen2.5-7B model via GRPO on this environment, showing measurable improvement across 6 decomposed reward components.

## The Problem

Your AI assistant double-books you, ignores your "no mornings" preference, and can't handle it when your boss reschedules a meeting at the last minute. This environment trains LLMs to actually handle real-world scheduling chaos.

## Architecture

- **Environment**: Procedurally generated scenarios with calendar conflicts, emails, user preferences, and late-breaking changes
- **3 difficulty tiers**: Easy (2 conflicts), Medium (4 conflicts + late changes), Hard (6 conflicts + 2 late changes)
- **6 reward components**: conflict resolution, preference inference, email quality, deadline adherence, efficiency, late-change recovery
- **All rewards are rule-based** — no LLM judges, fully deterministic and verifiable

## Quick Start

```python
from exec_assistant_arena import ExecAssistantArenaEnv, AssistantAction

with ExecAssistantArenaEnv(base_url="https://SidraMiconi-exec-assistant-arena.hf.space") as env:
    result = env.reset(seed=42, difficulty="medium")
    print(result.observation.tool_result)  # scenario description
    print(result.observation.conflicts)     # scheduling conflicts

    # Resolve a conflict
    result = env.step(AssistantAction(
        tool="reschedule",
        arguments={"event_id": "mtg_2", "new_time": "2:00pm"}
    ))
    print(f"Reward: {result.reward}")  # +1.0 for resolved conflict

    # Draft an email reply
    result = env.step(AssistantAction(
        tool="draft_reply",
        arguments={
            "email_id": "email_1",
            "body": "Hey! Sure thing, I'll get the budget review to you by tomorrow."
        }
    ))

    # Finish
    result = env.step(AssistantAction(tool="done"))
```

## Available Tools

| Tool | Arguments | Reward |
|------|-----------|--------|
| `check_calendar` | none | 0 (free) |
| `check_inbox` | none | 0 (free) |
| `reschedule` | `event_id`, `new_time` | +1.0 resolve, -0.5 new conflict |
| `draft_reply` | `email_id`, `body` | 0.0 to +1.0 (quality scored) |
| `delegate_task` | `task`, `to` | +0.5 if handles late change |
| `done` | none | terminal rewards |

## Training

Trained with GRPO (Group Relative Policy Optimization) using Unsloth + TRL:

```bash
# On H100
cd exec_assistant_arena
PYTHONPATH=. uvicorn server.app:app --host 0.0.0.0 --port 8000 &
python training/train_grpo.py
```

Colab notebook available at `training/train_colab.ipynb` for reproducing on free T4 GPU.

## Project Structure

```
exec_assistant_arena/
├── models.py                    # Action, Observation, State
├── client.py                    # WebSocket client
├── server/
│   ├── app.py                   # FastAPI server
│   ├── exec_assistant_arena_environment.py  # Core env logic
│   ├── scenario_generator.py    # Procedural generation
│   └── reward.py                # 6 decomposed reward components
└── training/
    ├── train_grpo.py            # H100 training script
    ├── train_colab.ipynb        # Colab version
    ├── eval.py                  # Before/after evaluation
    └── scenarios/
        ├── train_scenarios.json # 80 training scenarios
        └── eval_scenarios.json  # 20 held-out scenarios
```

## Links

- **HF Space**: https://huggingface.co/spaces/SidraMiconi/exec-assistant-arena
- **GitHub**: https://github.com/Sidra/chief-of-staff/tree/main/exec_assistant_arena
- **W&B**: https://wandb.ai/code-happy-sf/exec-assistant-arena
- **Trained Model**: https://huggingface.co/SidraMiconi/exec-assistant-arena-lora

Built for the OpenEnv Hackathon SF, March 7-8, 2026.