eagle0504/multireward-grpo-fintech-customer-comms

Multi-Reward GRPO — Synthetic Fintech Customer Communications

Synthetic multi-turn customer-service conversations for a fictional bank ("Bank of XYZ"), generated for the empirical Section of "Conditioned Multi-Reward Advantage Estimation: A Finite-Sample Analysis".

Each conversation ends with m parallel sampled bot replies, each scored on three verifiable reward channels designed for fintech customer service. This is the multi-reward GRPO group structure on a real generation distribution — analog to GSM8K but in a domain where multi-reward shaping matters most: compliance gating politeness/empathy.

Why fintech, why this structure

Fintech customer service is a high-stakes setting where the model's compliance (no unauthorized fee waivers, no leaking account details, no asking for SSN/passwords) is non-negotiable, and the easier-to-satisfy channels (politeness, brevity, clear next-step) only matter conditional on compliance. That's exactly the gating structure Proposition 4 in the paper analyzes, and it provides a non-math-reasoning anchor for the Theorem 3 correlation-floor claim.

Reward channels

Channel	Type	Definition	Role in paper
compliance	Bernoulli {0, 1}	Response avoids any of 9 patterns: unilateral fee waivers, claims about refund processing, leaks of account details, requests for sensitive info, phishing-shaped link pushes, etc.	"Harder gate" — analog of correctness on GSM8K
politeness_gated	Continuous [0, 1]	Sigmoid(empathy hits − 1.5·cold hits − 0.8), then multiplied by compliance	"Easier conditioned" — analog of length·correct
action	Bernoulli {0, 1}	Response ends with a clear next step (question, call-to-action, Y/N prompt)	Third channel for resolution (Prop 2)

Plus saved separately for the Prop 4 γ-sweep:

Field	Type	Definition
raw_politeness	Continuous [0, 1]	Politeness BEFORE the compliance gate
raw_length	Continuous [-1, 0]	`tanh(-

Diversity

• 15 scenario types: billing reminder (pay/delay), refund request, balance inquiry (compliance landmine — must verify ID first), lost/stolen card, fraud report, payment plan, address update, statement clarification, loan/credit-card application status, wire transfer confirmation, account cancellation, frustrated escalation, investment/IRA question, phishing test (compliance must NOT reveal info).
• 6 user personas: cooperative, anxious, frustrated, skeptical, time-pressed, confused.
• Randomized names, dollar amounts (spanning $5 to $100K), and due dates.
• Generated with Qwen2.5-7B-Instruct at temperature 0.8 to maximize reply diversity within scenarios.

File layout

fintech_rewards.npz             — main reward tensor (numpy)
  rewards        : (P, K, m, R=3) — (compliance, politeness_gated, action)
  raw_length     : (P, K, m)      — ungated length per rollout
  raw_politeness : (P, K, m)      — politeness BEFORE the compliance gate
  n_scenarios, K, m, reward_names, model (metadata)

fintech_metadata.json           — per-scenario type/persona/turns
fintech_sample_rollouts.json    — 50 random sample rollouts as text (inspect quality)
fintech_summary.json            — aggregate compliance/politeness/action rates

Quick load

import numpy as np
from huggingface_hub import hf_hub_download

p = hf_hub_download(
    repo_id="eagle0504/multireward-grpo-fintech-customer-comms",
    repo_type="dataset",
    filename="fintech_rewards.npz",
)
z = np.load(p, allow_pickle=True)
rewards = z["rewards"]            # (P, K, m, 3)
raw_pol = z["raw_politeness"]     # (P, K, m)

Provenance

Generated on a single NVIDIA H100 PCIe (80 GB) on RunPod Secure Cloud, using the open-access Qwen2.5-7B-Instruct model. No real customer data, no real bank, no real personally-identifying information. All conversation content is synthetic.

License

CC-BY-4.0.