README.md

metadata

title: Driver Recruit Environment
emoji: 🚛
colorFrom: blue
colorTo: green
sdk: docker
pinned: false
app_port: 8000
base_path: /web
tags:
  - openenv
  - reinforcement-learning
  - recruiting
  - multi-turn

🚛 Driver Recruit Environment

A multi-turn, tool-based RL environment for training LLMs to recruit truck drivers through a CRM system. Built on OpenEnv 0.2.1.

The agent must discover driver qualifications through conversation, record info in the CRM, get management approval, and hire — all using structured tool calls across 15-40+ step episodes.

Pipeline

lead → contacted → interested → approval_pending → offer_sent → hired

Tools

Tool	Actions	Purpose
crm	read_candidate, update_stage, update_field, add_note	Manage pipeline & record info
messaging	send_message, read_reply	Screen driver (18 topics)
approval	request_approval, check_approval	Get management sign-off
workflow	wait	Advance time for approval processing

Reward Signal

• Successful hire (good job fit): +10 to +15 (base + CRM bonus)
• Bad hire (poor match): -5
• Ghosted (trust runs out): -4
• Per-step: Small rewards/penalties for correct/incorrect actions

What Makes This Hard

• Long horizon: 15-40+ tool calls per episode
• Information gathering: Must ask the right screening questions to match driver to the right job
• Trust dynamics: Each message costs trust — ask too many questions and the driver ghosts
• Job matching: 6 jobs per episode (1-2 good, 1-2 traps with deal-breakers, 2-3 partial)
• Procedural correctness: Must follow stage order, read replies before messaging, get approval before offering

Quick Start

from recruitopenenv import RecruitopenenvEnv, RecruitopenenvAction

env = RecruitopenenvEnv(base_url="YOUR_SPACE_URL")

result = env.reset(seed=42)
obs = result.observation
print(f"Driver: {obs.driver_name}, Stage: {obs.stage}")

# Read CRM
result = env.step(RecruitopenenvAction(tool="crm", action="read_candidate"))
print(result.observation.jobs_summary)

# Greet driver
result = env.step(RecruitopenenvAction(tool="messaging", action="send_message", topic="greeting"))
print(f"Reward: {result.reward}")

# Read reply
result = env.step(RecruitopenenvAction(tool="messaging", action="read_reply"))
print(result.observation.discovered_info)

env.close()

Training

We train using GRPO/REINFORCE with the model choosing screening topics. See train_grpo.py for the full training script.

python train_grpo.py --model Qwen/Qwen2.5-3B-Instruct

Deploying

# From the recruitopenenv/ directory
openenv push

Action Format

{"tool": "crm", "action": "read_candidate"}
{"tool": "messaging", "action": "send_message", "topic": "experience"}
{"tool": "messaging", "action": "read_reply"}
{"tool": "crm", "action": "update_field", "field": "cdl_class", "value": "A"}
{"tool": "crm", "action": "update_stage", "stage": "contacted"}
{"tool": "approval", "action": "request_approval", "job_id": 2}
{"tool": "workflow", "action": "wait"}
{"tool": "approval", "action": "check_approval"}
{"tool": "messaging", "action": "send_message", "topic": "offer", "job_id": 2}
{"tool": "crm", "action": "update_stage", "stage": "hired"}

Observation Fields

Field	Description
driver_name	Driver's name
crm_summary	Full CRM record (empty until read_candidate)
jobs_summary	6 available job listings
discovered_info	Info from screening conversations
stage	Current pipeline stage
feedback	API response from last action
pending_reply	Whether driver has unread message

Screening Topics

greeting, call, experience, home_time, pay, equipment, route, deal_breakers, availability, violations, medical_card, references, pitch, offer, negotiate_pay, negotiate_home_time, signing_bonus, address_concern