PEER: Data for Structured Empathetic Reasoning

Data accompanying PEER: Unified Process–Outcome Reinforcement Learning for Structured Empathetic Reasoning. Four subsets cover the full pipeline: reward-model supervision (SER), supervised fine-tuning (SFT), reinforcement learning prompts (GRPO), and a safety probe suite.

Training and evaluation code: github.com/Yunxiao-Wang/PEER.

Contents

Subset	Rows	Languages	Images	Purpose
ser/	21,868 labels over 5,467 dialogues	en + zh	988	reward-model training and evaluation
sft/	6,047	en + zh	410	supervised fine-tuning
grpo/	14,706 (9,998 textual / 4,708 multimodal)	en + zh	9,416	RL prompt pool
safety/	190	en + zh	–	high-risk probe suite

All records are JSON Lines. images fields hold paths relative to their subset directory, so ser/en/visual_train.jsonl referencing images/dia11572utt2_17.jpg resolves to ser/images/dia11572utt2_17.jpg.

Download

from huggingface_hub import snapshot_download

root = snapshot_download("YunxiaoWang/PEER", repo_type="dataset")

Fetch a single subset instead of all 340 MB:

root = snapshot_download("YunxiaoWang/PEER", repo_type="dataset",
                         allow_patterns=["safety/*"])

Read any subset line by line:

import json, os

with open(os.path.join(root, "ser/en/visual_train.jsonl")) as f:
    for line in f:
        rec = json.loads(line)
        images = [os.path.join(root, "ser", p) for p in rec.get("images", [])]

ser/ — Structured Empathetic Reasoning

Fine-grained supervision for the reward model: for each dialogue turn it labels the correctness of three intermediate reasoning steps and a pairwise preference over two candidate supporter replies.

Each line is a fixed 9-message, 4-round chat in the messages format.

• messages[0] — system: the task spec. Rounds 1–3 judge each analysis step as correct or incorrect; round 4 picks the better of two candidate replies.
• Rounds 1–3 — user turn gives the conversation history plus one reasoning step; assistant turn is the verdict (True / False, zh 正确 / 错误).
• Round 4 — user turn gives the two candidate replies; assistant turn is the preference verdict (The first sentence is better / The second sentence is better / The two sentences are almost good / Neither sentence is available; zh 第一句更好 / 第二句更好 / 差不多 / 都不可用).

field	meaning
messages	the 9-message chat above
id	integer id, unique across all four subsets
lang	en or zh
source	original (human-annotated) or augmented (personality-based rewriting, train only)
images	visual rows only: one path under images/

5,467 annotated dialogues (4,904 train / 563 test), each carrying four labels — three step verdicts and one response preference — for 21,868 labels in total. Inter-annotator agreement is Fleiss κ = 0.69 for step labels and 0.56 for response preferences.

The files hold 15,135 records: the 5,467 annotated dialogues plus 9,668 personality-rewritten copies (source: augmented, train only). The 563 test records are the reference set for the reward-model results in the paper.

file	records		file	records
en/text_train.jsonl	4,723		zh/text_train.jsonl	4,624
en/visual_train.jsonl	2,647		zh/visual_train.jsonl	2,578
en/text_test.jsonl	182		zh/text_test.jsonl	178
en/visual_test.jsonl	102		zh/visual_test.jsonl	101

Chinese preference labels: 第一句更好 2,800 · 第二句更好 3,086 · 差不多 803 · 都不可用 792.

sft/ — supervised fine-tuning

6,047 examples built from SER, pairing the structured three-stage instruction with the reference supporter reply. Targets carry the explicit Step 4: Response Generation. marker (zh 步骤4： 回复生成。) that the format reward checks for.

grpo/ — RL prompt pool

Dialogue-continuation prompts for training the RL variants with GRPO.

field	meaning
messages	the structured three-stage instruction
history	the dialogue so far
solution	the reference supporter reply
language	english or chinese
modality	textual or visual
source	originating corpus
images	visual rows only: two face frames of the seeker

split	rows	languages	sources	avg. turns	avg. reply words
textual.jsonl	9,998	en 4,998 / zh 5,000	ExTES 4,178 · SoulChat 4,045 · Smile 877 · ESConv 592 · ESCoT 228 · PsyDT 78	7.3	18.8
visual.jsonl	4,708	en	AvaMERG	2.4	14.2

images/ holds 9,416 frames, two per visual row, taken at 1/2 and 3/4 of each utterance clip.

Each GRPO run in the paper samples 2,000 textual and 1,000 multimodal prompts from this pool.

safety/ — high-risk probes

190 probes, built from 87 English crisis conversations (Between Help and Harm), 90 Chinese high-risk expressions (PsyCrisis-Bench), and 60 probes written for this work. A judge relabeled every public probe as a crisis disclosure, a harmful request, or an ordinary conversation; ordinary conversations were dropped and harmful requests merged into the self-written set.

group	count
crisis disclosure	104
harmful request	56
boundary	30

field	meaning
id	probe id
history	the seeker turns
category	fine-grained label from the source benchmark
group	crisis / harmful_request / boundary
source	originating benchmark or self-built

The self-written probes share no 4-gram with the training pool.

Provenance

The dialogues derive from nine public emotional-support corpora. Face frames come from the public AvaMERG videos (ZhangHanXD/AvaMERG). Each source keeps its own terms; consult them before use.

Corpus	Modality	Used in
ESConv	text	SER, GRPO
MESC	text + face	SER
AvaMERG	text + face	SER, GRPO
SoulChat	text	SER, GRPO
PsyDTCorpus	text	SER, GRPO
CPsyCoun	text	SER
SmileChat	text	SER, GRPO
ExTES	text	SER, GRPO
ESCoT	text	SER, GRPO

Reasoning steps and candidate replies were generated with GPT-4o and then labeled by 20 trained annotators recruited through a crowdsourcing platform, who received reasonable compensation.

Intended Use

The "emotional support" studied here provides non-professional comfort and companionship in the manner of a friend or family member. It does not provide psychological counseling or diagnosis. This data is released for academic research only.

Records describe emotional distress, and the safety subset deliberately contains crisis disclosures and requests for harmful information. Handle accordingly.

Citation

@article{Wang2025PEER,
  author       = {Yunxiao Wang and
                  Meng Liu and
                  Sicheng Zhao and
                  Lizi Liao and
                  Liqiang Nie},
  title        = {{PEER:} Unified Process--Outcome Reinforcement Learning for Structured Empathetic Reasoning},
  journal      = {arXiv},
  volume       = {abs/2508.09521},
  year         = {2025}
}