Supervisor-FRPT — Supervisor-FRPT-Phi-4-reasoning

This model is a LoRA-fine-tuned supervisor (CS quality evaluator) for electronics customer-support chatbot conversations. It was trained on 20260331_HumanFeedBack_selfdist.jsonl (3,771 human-labelled dialogues) with the FRPT ("Fact-Reasoning Process Training") research training methodology applied to a lora_sequential LoRA recipe.

The job of this model: given (category, multi-turn user/assistant transcript, retrieved reference document), produce a Korean <think>...</think> rubric chain and a JSON verdict {"label": "correct|incorrect", "reason": "..."}.

Test metrics (held-out 199 dialogues)

Training methodology — research highlights

The training methodology bundles two layers:

1. Base LoRA recipe — lora_sequential with rank 16, alpha 32, dropout 0.05, target modules qkv_proj, o_proj, down_proj, gate_up_proj (Phi-3 family) or the q/k/v/o/MLP equivalents for Gemma-4. Optimizer AdamW, cosine schedule, warmup ratio 0.05, grad clip 1.0, BF16, SDPA attention.

2. FRPT-aware data shaping (Fact-grounded Reasoning Process Training):

   • Process-supervision view — the assistant turn already exposes a 3-axis rubric (Query-Document Alignment, Response-Document Consistency, Response Completeness) inside <think>...</think>. We train the entire assistant response, so the model learns the reasoning process, not just the verdict.
   • Fact-grounded SFT — loss is masked on user/system tokens; only the assistant span (think + JSON) contributes to gradient. This forces the model to learn how to evaluate, not what the user said.
   • Class-imbalance aware — incorrect : correct = 2616 : 1155 (~2.3:1) in train. We monitor F1-correct (the minority class) as the primary model-selection signal.
   • (Sequential variant) — lora_sequential groups the 33 product categories into 5 buckets (DRW, TV, SBS, REF_AUD_MNT, OTHERS) and trains them in order, exposing the model to per-category structure while sharing one adapter across the curriculum.

Hyperparameters of the final run

Quick inference

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

mid = "shareit/Supervisor-FRPT-Phi-4-reasoning"
tok = AutoTokenizer.from_pretrained(mid, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(mid, dtype=torch.bfloat16,
                                            device_map="auto",
                                            trust_remote_code=True)

system = "당신은 전자제품 CS 챗봇의 품질을 평가하는 수퍼바이저입니다."
user = ("[Category] PC\n\n[Conversation Transcript]\n"
        "Turn 1 - User: ...\nTurn 1 - Assistant: ...\n\n"
        "[Retrieved Document]\n(title) ...\n(content) ...")

msgs = [{"role": "system", "content": system},
        {"role": "user", "content": user}]
inp = tok.apply_chat_template(msgs, tokenize=True, add_generation_prompt=True,
                              return_tensors="pt").to(model.device)
out = model.generate(inp, max_new_tokens=900, do_sample=False)
print(tok.decode(out[0, inp.shape[1]:], skip_special_tokens=True))

The generated text follows:

<think>
[Query-Document Alignment] ...
[Response-Document Consistency] ...
[Response Completeness] ...
</think>
{"label": "correct", "reason": "..."}

Citation / theory

This model embodies the FRPT (Fact-Reasoning Process Training) research program. Key references that inform the methodology:

• Gekhman et al. 2024 — fine-tuning new facts can encourage hallucination.
• Lightman et al. 2023 — Let's Verify Step by Step (process supervision).
• Hu et al. 2021 — LoRA.
• Dettmers et al. 2023 — QLoRA.
• LoRA Learns Less and Forgets Less (Biderman et al.) — PEFT/FullFT tradeoffs.

For the merge-before-forget continual-learning theory that motivated the sequential variant, see the internal Session 1~4 reports.