email-triage-grpo

📧 Email Triage Agent — GRPO Fine-tuned (Llama-3.1-8B)

A LoRA adapter for meta-llama/Llama-3.1-8B-Instruct, trained using GRPO (Group Relative Policy Optimization) to autonomously triage emails — assigning priority, category, and action with human-level accuracy.

GRPO is the same RL technique used in DeepSeek-R1. Instead of supervised labels, the model learns by receiving reward signals from an environment — just like how humans learn from feedback.

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🧠 What This Model Does

Given an email (subject + sender + body), the model outputs:

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🏋️ Training Details

Reward Function

The model was trained on a reward signal (not supervised labels):

reward = priority_score × 0.35
       + category_score × 0.25  
       + action_score   × 0.30
       + sentiment_bonus         (up to +0.08)
       + tag_bonus               (up to +0.08)
       - sla_breach_penalty      (-0.15 if urgent email archived)
       - urgent_archive_penalty  (-0.35)

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🚀 Quick Start

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base_model = "meta-llama/Llama-3.1-8B-Instruct"
adapter    = "Adityasharma4287/email-triage-grpo"

tokenizer = AutoTokenizer.from_pretrained(adapter)
model = AutoModelForCausalLM.from_pretrained(base_model, device_map="auto")
model = PeftModel.from_pretrained(model, adapter)

email = """
Subject: URGENT - Server is DOWN
From: [email protected]
Body: Our entire application is down. 50,000 users affected. 
Need immediate response from engineering.
"""

messages = [
    {"role": "system", "content": "You are an expert email triage agent. Classify the email and decide the best action."},
    {"role": "user",   "content": f"Triage this email:\n{email}\n\nRespond with JSON: priority, category, action, notes"}
]

input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
output = model.generate(input_ids, max_new_tokens=256, temperature=0.1)
print(tokenizer.decode(output[0][input_ids.shape[-1]:], skip_special_tokens=True))

Expected output:

{
  "priority": "urgent",
  "category": "technical", 
  "action": "escalate",
  "notes": "Production outage affecting 50k users. Immediate engineering response needed.",
  "custom_tags": ["VIP"]
}

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📊 Benchmark Results

Scores measured on Email Triage OpenEnv v2.0 with seed=42

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🌐 Live Demo & Environment

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🆕 Advanced Features (v2.0 Environment)

The model was trained on an advanced environment with:

• ⏱ SLA Tracking — urgent emails with ≤2h deadlines trigger extra penalties
• 🧠 Sentiment Scoring — angry customers (-0.95 score) get urgency boost
• 🏷️ Custom Tags — VIP, churn-risk, legal-review, follow-up
• 💤 Snooze Action — new action type for low-priority deferrals
• 🌐 Multilingual — Spanish email test cases included
• ⚖️ Legal/HR Categories — GDPR requests, lawsuit threats, HR emails

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📁 Repository Structure

email-triage-grpo/
├── adapter_config.json       ← LoRA config
├── adapter_model.safetensors ← Trained weights (27.3 MB)
├── tokenizer.json            ← Llama-3.1 tokenizer
├── tokenizer_config.json     
├── chat_template.jinja       ← Chat prompt template
├── env/                      ← Training environment
│   └── email_triage_env.py  ← OpenEnv environment
├── smart_agent.py            ← Agent inference script
└── inference.py              ← Inference utilities

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🔁 Training Your Own Agent

The full training environment is included. Run your own GRPO training:

git clone https://huggingface.co/Adityasharma4287/email-triage-grpo
cd email-triage-grpo
pip install -r requirements.txt

# Set env vars
export API_KEY="your-openai-or-local-key"
export MODEL_NAME="gpt-4o-mini"  # or your local model
export ENV_BASE_URL="http://localhost:7860"

# Start the environment server
python app.py &

# Run the smart agent
python smart_agent.py

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📜 Citation

@misc{email-triage-grpo-2026,
  title   = {Email Triage Agent: RL Fine-tuning with GRPO on Llama-3.1-8B},
  author  = {Adityasharma4287},
  year    = {2026},
  url     = {https://huggingface.co/Adityasharma4287/email-triage-grpo},
  note    = {LoRA adapter trained with Group Relative Policy Optimization}
}

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⚠️ Limitations

• Trained on synthetic email templates — real-world email diversity may differ
• Best results with structured JSON output prompting
• Base model (Llama-3.1-8B) requires ~16GB VRAM for full inference; use 4-bit quantization for smaller GPUs

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Built with ❤️ using GRPO + LoRA + OpenEnv