StageWell: A Process-Aligned Chinese Corpus for Positive-Psychology Support Dialogue

Yuxiong Wang; Ziwei Lin; Bo Wang; Yu Zhang; Shiguang Ni

cs.CLAug 29, 2026

StageWell: A Process-Aligned Chinese Corpus for Positive-Psychology Support Dialogue

Authors: Yuxiong Wang, Ziwei Lin, Bo Wang, Yu Zhang, Shiguang Ni

Organizations: Shenzhen International Graduate School, Tsinghua University · Department of Psychological and Cognitive Sciences, Tsinghua University

Abstract

Positive psychology dialogue aims to support emotional distress and positive resource building, requiring models to produce not only empathetic replies but also coherent progression through a multi-turn support process. Existing resources often reduce supervision to turn-level strategies or holistic preference labels, leaving process position, support function, and local repair targets implicit. We introduce StageWell, a process-aligned Chinese corpus for positive psychology dialogue, together with HQS, a structured protocol for data construction and evaluation. StageWell organizes support into a six-stage support process and uses a multi-agent whole-dialogue rewriting workflow to construct 12,445 SFT instances, 1,849 DPO preference pairs, and a GroundTruth subset of 120 expert-revised dialogues and 977 QA pairs. Guided by HQS, DPO pairs are built as process-localized repairs: flawed model outputs are used as rejected responses, and targeted rewrites under the same context and stage constraint are used as chosen responses. Across four 9B-14B open-source LLMs, this supervision yields robust gains in process control, response quality, and safety. Averaged across models, BERTScore improves by 0.037, Q-Overall increases by 1.32 points, S-exact increases by 0.236, and the H-critical rate decreases by 0.167. These results highlight the value of modeling supportive dialogue as a structured multi-turn support process rather than as single-turn response generation.

Figures & tables

Figure 1: Overview of the six-stage support process for positive psychology dialogue.

Figure 2: Overview of the StageWell framework. The left panel shows data preparation and the six-stage support process; the middle panel shows multi-agent whole-dialogue SFT construction; the upper-right panel shows phased alignment training with HQS-guided DPO preference alignment; and the bottom panel shows HQS-based evaluation.

Stage	Primary Objective	Dialogue Strategy and Focus
S1 Rapport & Safety	Trust and emotional safety	Warm opening and low-pressure invitation.
S2 Clarify & Reflect	Precise concern understanding	Listening, reflection, and open-ended clarification.
S3 Strengths & Resources	Resource awareness	Coping experiences, strengths, and relationships.
S4 Future & Hope	Realistic hope and possibility	Future-oriented reframing without overpromising.
S5 Goals & Strategies	Feasible next steps	Small, collaborative, context-grounded actions.
S6 Summary & Transfer	Consolidation and continuation	Brief summary, reinforcement, and follow-up space.

Table 1: The six-stage support process used in StageWell.

Agent	Role and Responsibility
Planner	Stage plan, facts, and turn functions.
Writer	Plan-guided coherent rewriting.
Patcher	Repetition, transition, and consistency repair.
Refiner	Fluency polishing with stage intent kept.

Table 2: Roles within the multi-agent rewriting pipeline.

Figure 3: DPO preference profile across six stages, showing the distribution of Q1–Q5 repair signals and response length differences. Q1–Q5 denote content fit, acknowledgment, feasibility, maturity, and Chinese naturalness.

Module	Operational criterion
H	Hard constraints : safety and risk checks, including role-boundary violations.
Q	Response quality : content fit, emotional acknowledgment, feasibility, maturity, and Chinese naturalness.
S	Stage consistency : expected stage, progression pace, and transition fit.

Table 3: HQS modules used in preference construction and model evaluation.

A. Resource scale and basic statistics
Subset	Statistic	Value
SFT	Training instances	12,445
SFT	Avg. turns / characters	8.09 / 617.51
SFT	Stage-label consistency	100.00%
DPO	Preference pairs / source dialogues	1,849 / 959
DPO	Avg. rejected / chosen chars	46.17 / 44.10

Table 4: Detailed statistics of the StageWell subsets. S1–S6 denote support stages, and Q1–Q5 denote HQS response-quality dimensions.

Model	Cond.	Q: Response Quality $\uparrow$						H $\downarrow$	S: Stage Consistency $\uparrow$
Model	Cond.	Overall	Q1	Q2	Q3	Q4	Q5	Critical	Exact	Top-2	Within-1	Macro-R
Qwen3-14B	Base	3.562	3.414	3.873	2.984	3.560	3.589	0.058	0.419	0.559	0.652	0.396
	SFT	3.971	3.766	4.386	3.878	4.306	3.869	0.050	0.476	0.589	0.743	0.457
	DPO	4.232	4.137	4.481	4.382	4.508	4.142	0.033	0.515	0.614	0.765	0.477
GLM-4-9B	Base	3.301	3.273	3.423	2.455	2.940	3.359	0.033	0.402	0.506	0.628	0.381
	SFT	3.794	3.731	3.951	4.150	3.934	3.731	0.033	0.680	0.792	0.820	0.659

Table 5: HQS results on GroundTruth. Q1–Q5 are the five HQS quality dimensions; lower H-critical is better.

Base Model	Condition	ROUGE-1	ROUGE-2	ROUGE-L	BLEU-4	BERTScore	Avg.
Qwen3-14B	Base	0.334	0.115	0.234	0.098	0.292	0.215
	SFT	0.357	0.121	0.247	0.103	0.295	0.225
	DPO	0.373	0.141	0.270	0.118	0.314	0.243
Gemma-3-12B	Base	0.132	0.052	0.097	0.035	0.211	0.105
	SFT	0.351	0.118	0.245	0.097	0.291	0.220
	DPO	0.354	0.124	0.251	0.103	0.296	0.226

Table 6: Reference-based automatic evaluation on 977 GroundTruth QA pairs. All overlap scores are shown on a 0–1 scale. Avg. is the unweighted mean of ROUGE-1/2/L, BLEU-4, and BERTScore. Bottom rows summarize averages across backbones and the DPO–Base improvement.

A. Interaction statistics
Valid participants	28
Avg. interaction turns	$7.14\pm 0.77$
Avg. usage time (minutes)	$9.57\pm 1.87$

Table 7: Pilot user-study results. Reverse-coded dimensions are reported in the positive direction. Positive denotes the percentage of participants scoring at least 4 on a five-point Likert scale.

Figure 4: Visualization of the pilot user-study results. Panel A shows pre–post changes in immediate psychological state, and Panel B summarizes post-interaction subjective experience.

Appendix figures & tables38 assets

Supplementary material from the paper’s appendix.

Appendix

Topic	Count	Prop.	Typical coverage
Peer relationships and campus social life	4,827	38.8%	Friend conflict, exclusion, bullying, group pressure, social distance.
Academic pressure and learning strategies	2,474	19.9%	Homework load, exams, rank anxiety, review planning, teacher interaction.
Family communication and support	1,818	14.6%	Parent-child communication, parental expectations, misunderstanding, family support.
Goal and strength development	768	6.2%	Goal setting, interests, strengths, self-motivation, future direction.
Emotion and stress management	623	5.0%	General anxiety, sadness, helplessness, emotional pressure.
Self-concept and self-esteem	541	4.3%	Low confidence, self-doubt, appearance concerns, comparison, self-worth.

Appendix

Table 8: Topic taxonomy and final SFT topic distribution. Counts sum to 12,445.

Figure 5: Thematic and contextual coverage of the SFT corpus. The inner ring shows high-level support themes, the outer ring shows representative contextual scenarios, and the side legend reports theme volumes across the 12,445 SFT training instances.

Tool	Required slot	Stage	Purpose
check_in	main_pain	S1	Confirm the main concern and emotional load.
trigger_scene	recent_scene	S2	Elicit a recent concrete triggering scene.
body_signal	body_signal	S2	Add bodily or pressure-related cues when explicitly grounded.
self_talk	inner_talk	S2	Clarify the user’s current thought or self-talk.
stakes	worst_case	S2	Clarify feared consequences or perceived stakes.
strength_exception	past_success	S3	Retrieve past coping experiences or exceptions.

Appendix

Table 9: Dialogue-tool and information-slot mapping used in the stage-aware rewriting skeleton.

Figure 6: SFT prompt template used in planner-guided whole-dialogue rewriting.

Module	Primary output	Core responsibility	Hard boundary
Case Card (heuristic)	topic, tags, emotion, goal, risk	Build a stable student-facing scenario anchor before generation.	High-risk cases are routed out before ordinary rewriting.
Planner	target_rounds, step_schedule, facts, skeleton, close_hint	Convert the source dialogue into an ordered six-stage plan with evidence-linked turn functions.	Must preserve topic class, monotonic stage order, and adolescent-scenario normalization.
Writer	rewrite[] with step labels	Generate the whole rewritten dialogue under the shared skeleton.	Cannot change stage assignment, round count, or required slots.
JsonFix	repaired JSON or explicit failure mark	Recover truncated or malformed structured outputs with minimal edits.	May repair format only; no semantic rewriting.
Patcher	locally rewritten marked rounds	Repair repetition, near-duplicate questioning, or transition roughness.	Must keep the same step, facts, and required slot.
Refiner	polished rewrite[]	Improve naturalness and coherence at the discourse level.	Cannot add/delete turns or drift into adult/clinical framing.

Appendix

Table 10: Generative and deterministic modules in the full SFT rewriting workflow.

Planner field	Operational meaning
target_rounds	Desired dialogue length after rewriting, constrained to be no shorter than the original usable interaction.
step_schedule	Ordered six-stage route indicating which support stage each assistant turn should serve.
close_hint	Final-turn closing intention used to keep the S6 ending brief and non-interrogative.
facts	Normalized situation facts that downstream modules may rely on; these are adolescent-adapted and topic-aligned.
skeleton[t].q_type	Dialogue tool assigned to assistant turn $t$ , e.g., trigger_scene or micro_step .
skeleton[t].must_add_slot	Required information slot that must appear in turn $t$ if the tool is realized correctly.

Appendix

Table 11: Planner-side structured fields retained in the rewriting skeleton.

Audit artifact	Illustrative retained content
Case Card	Topic: academic pressure; emotion: frustration + shame; user goal: reduce avoidance and regain a manageable first step; risk: ordinary.
Planner fields	target_rounds =8; step_schedule =[S1,S2,S2,S3,S4,S5,S5,S6]; close_hint : briefly consolidate the student’s manageable next step.
Retained facts	F1: the student feels unprepared for an upcoming exam; F2: panic increases when thinking about unknown questions; F3: previous preparation routines helped; F4: teacher feedback is available as support.
Skeleton tool mapping	Early turns clarify the concrete trigger and self-talk; middle turns activate prior strengths and a preferred future image; later turns construct a micro-step and summarize one transferable takeaway.
Rewritten dialogue excerpt	User: “I feel blank whenever I think about the exam.” Assistant: validates the pressure, asks for a recent trigger, retrieves a prior coping exception, proposes a 15-minute review step, and ends with a brief non-interrogative transfer summary.
Validator decision	Accepted: role alternation holds, stages are monotonic, no unsupported facts are introduced, and the final assistant turn is non-interrogative.

Appendix

Table 12: Illustrative audit trail showing the kinds of intermediate artifacts retained by the construction and evaluation pipeline. This table is schematic rather than a verbatim sample dump.

Group	Diagnostic	Value
Scale	SFT instances	12,445
Length	Avg. turns per sample	8.09
Length	Avg. characters per sample	617.51
Length	Avg. user-turn characters	24.38
Length	Avg. assistant-turn characters	51.96
Length	Assistant/user length ratio	2.13

Appendix

Table 13: Additional structural diagnostics for the SFT subset.

Revision dimension	What experts were asked to improve
Context fidelity	Preserve the original topic, user concern, and situational facts unless the draft contains explicit distortion.
Stage intent	Keep the intended support function of each assistant turn rather than freely rewriting the dialogue strategy.
Educational fit	Replace adult, clinical, or over-formal wording with school-appropriate and adolescent-facing language.
Empathic adequacy	Strengthen emotional acknowledgment when the draft moves too fast into explanation or advice.
Action realism	Replace generic suggestions with lower-burden, more concrete, and context-grounded next steps.
Boundary safety	Remove role overreach, diagnosis-like wording, or advice inconsistent with non-clinical support.

Appendix

Table 14: Main dimensions emphasized during expert revision of GroundTruth drafts.

Hyperparameter	SFT	DPO
Initialization	Public backbone	Backbone + LoRA
Framework	ms-swift	ms-swift
Task	LoRA SFT	LoRA DPO
Precision	BF16	BF16
Max seq length	1024	1536
Optimizer	AdamW	AdamW

Appendix

Table 15: Training hyperparameters for SFT and DPO.

Dim.	Core judgment	Stages
Q1 Content fit	Addresses explicit concern without topic drift or over-reading.	S1-S6
Q2 Acknowledgment	Catches user’s emotion and vulnerability before advice.	S1-S6
Q3 Feasibility	Realistic, concrete, low-burden, immediately actionable.	S5
Q4 Maturity	Natural, proportionate, experientially plausible.	S5,S6
Q5 Chinese naturalness	Fluent, conversational, non-template-like Chinese.	S1-S6

Appendix

Table 16: Q-dimension definitions and stage applicability within the six-stage support process.

Dim.	5-point anchor	3-point anchor	1-point anchor
Q1	Tightly addresses the current user turn; no topic drift or unsupported inference.	Broadly relevant but misses the specific focus or includes mild over-reading.	Off-topic, misunderstanding, or treats unstated content as fact.
Q2	Catches the user’s emotion and vulnerability with supportive tone.	Shows acknowledgment but remains surface-level.	Cold, judgmental, didactic, or moves to advice before acknowledgment.
Q3	Gives a concrete, realistic, low-burden next step.	Gives a direction but is vague, high-burden, or hard to execute.	Abstract, idealized, or leaves the user unsure what to do next.
Q4	Advice is natural, steady, proportionate, and life-like.	Direction is reasonable but stiff, generic, or lacking lived-situation nuance.	Mechanical, scripted, over-designed, or forced.
Q5	Fluent, natural, and conversational Chinese.	Understandable but bookish, template-like, or mildly awkward.	Stilted, unnatural, overly ornate, or AI-like.

Appendix

Table 17: Q-score anchors used by the LLM-as-a-Judge evaluator. Intermediate scores 2 and 4 are used for cases between adjacent anchors.

Figure 7: HQS judge prompt and output schema.

Judge module	Primary JSON fields	What the prompt asks it to do	What the prompt explicitly forbids
Hard Judge (H)	flags, alignment_score, evidence, notes	Inspect the whole dialogue for hallucination, safety risk, role violation, and boundary overreach; provide short evidence spans.	No free-form essay; no stage scoring; no evidence invented beyond assistant text spans.
Quality Judge (Q)	applicable_dims, scores, dimension_notes, evidence_by_dim, confidence	Score only the candidate assistant turn on the Q dimensions valid for the expected stage.	Must not respecify the stage, score historical turns, or rate non-applicable dimensions.
Stage Judge (S)	pred_step_name, confidence, secondary_step_name	Predict the dominant support stage realized by the current assistant turn.	Must output one primary stage only; no broad explanation beyond the schema.
JSON Repair	repaired JSON or failure object	Repair malformed judge outputs into valid JSON with minimal editing.	Cannot revise the substantive judgment or add new analytical content.

Appendix

Table 18: Operational contracts of the four modules in the LLM-as-a-Judge stack.

Schema rule	Purpose in evaluation
Evidence spans must be short	Keeps judgments auditable and reduces post-hoc rationalization.
Non-applicable Q dimensions are null	Prevents the evaluator from fabricating relevance for stages where a dimension is not meaningful.
Confidence is retained separately	Allows downstream scripts to inspect uncertain stage predictions without changing the main score definition.
JSON Repair is content-neutral	Ensures parsing recovery does not inflate or alter model performance.
Candidate reply is isolated	Encourages true local scoring instead of diffuse holistic impressions.

Appendix

Table 19: Judge-side schema conventions motivated by auditability and stability.

Target	User turn	Rejected response	Chosen response
Q1 content fit	A classmate knocked over my water and half my workbook got wet. I shouted and the whole class looked at me.	You must have been tense; maybe your palms got sweaty and your chest felt tight.	The water spilled over half your workbook, you shouted, and everyone looked at you. That moment could easily make you explode.
Q2 acknowledgment	I studied all week, but I still did badly on the exam. I really do not want to talk to anyone now.	First make a wrong-question list and reset your study plan for the next exam.	You worked hard all week and still got this result, so it makes sense that you feel upset and do not want to talk right now.
Q3 feasibility	If my dad reaches for my homework again, I will touch the eraser in my pocket.	That little eraser is quietly saying, “I hear myself, and I am here.”	Next time he reaches over, touch the eraser in your pocket for three seconds. You do not need to say anything; just do those three seconds first.
Q4 maturity	Okay, I will try.	Next time you hear gossip, lightly touch your earlobe like pressing a pause button.	Next time you hear that kind of gossip, first bring your attention back to what you are doing, then decide whether you want to respond.
Q5 Chinese naturalness	When I play basketball and get blocked, I do not get angry and can still laugh.	When playing basketball, is your whole body loose or tight?	When you get blocked in basketball, you actually do not feel that tense, right?

Appendix

Table 20: Illustrative process-localized repair examples. Rejected responses exhibit the localized defect targeted by the Q dimension, and chosen responses repair it under the same context and stage constraint.

Target	Primary rewrite instruction	What must not happen during repair
Q1	Re-align the reply to the most explicit content of the current user turn; remove unsupported detail completion and topic drift.	Do not add inferred body reactions, inner speech, hidden motives, or new situational facts.
Q2	Strengthen emotional acknowledgment before advancing the task; make the user feel “received” first.	Do not become preachy, overly explanatory, teasing, or theatrically gentle.
Q3	Compress the advice into a concrete, low-burden, immediately doable next step that fits the current scene.	Do not expand into a checklist, generic self-help package, or abstract encouragement.
Q4	Keep the intended direction but rewrite it as something a mature, believable adult would naturally say in conversation.	Do not rely on symbolic rituals, over-designed metaphors, script-like mini exercises, or stock reassurance.
Q5	Preserve meaning while rewriting the sentence into plain, natural, conversational Chinese.	Do not pursue literary freshness at the cost of naturalness; remove “AI-like” wording and stiff template phrasing.

Appendix

Table 21: Dimension-specific rules for the targeted DPO rewrite prompt. Each chosen response is intended to repair one localized defect rather than globally rewrite the entire answer.

Shared prompt rule	Reason
Fix only one target dimension at a time	Prevents preference ambiguity and preserves localized supervision.
Keep reply length close to the original	Reduces accidental length preference and verbosity bias.
Preserve stage function	Ensures DPO does not learn a different support step than intended.
Avoid new safety or factual risks	Stops local repairs from introducing boundary regressions.
Output only the rewritten assistant turn	Simplifies export into chosen/rejected training format.

Appendix

Table 22: Shared constraints in the DPO rewrite prompt regardless of target Q dimension.

Figure 8: Abridged prompt card for targeted DPO localized repair. The rewrite keeps the dialogue context and target stage fixed while repairing only the designated Q dimension.

Diagnostic	Count	Prop.
Single low-quality dimension	729	39.43%
Two low-quality dimensions	88	4.76%
Three low-quality dimensions	1,032	55.81%
Most common combo: Q1,Q2,Q5	762	41.21%
Chosen shorter than rejected	989	53.49%

Appendix

Table 23: Additional diagnostics for DPO preference pairs.

Stage	Chosen shorter
S1 Rapport & Safety	54.66%
S2 Clarify & Reflect	55.56%
S3 Strengths & Resources	51.63%
S4 Future & Hope	61.19%
S5 Goals & Strategies	$\approx$ 46.0%
S6 Summary & Transfer	59.02%

Appendix

Table 24: Length-alignment diagnostic by target stage.

Figure 9: Additional visualizations of reference-based and process-aware evaluation results across Base, SFT, and DPO conditions.

Stage	Operation	Purpose
Pre-test	Fill in immediate-state scale.	Record state before interaction.
Free interaction	Multi-turn supportive dialogue.	Provide short-term support.
Post-test I	Fill in the same state scale.	Compare pre/post state.
Post-test II	Fill in subjective-experience scale.	Evaluate feasibility and usability.

Appendix

Table 25: Feasibility and usability pilot procedure.

Figure 10: Reconstructed participant-information and informed-consent summary used in the pilot. Blue text : key study metadata. Red text : major consent dimensions. Green background : participant protections, intended support scope, and withdrawal rights. Yellow background : low-risk and anonymized-reporting statements.

Dimension	Item
Stress burden (rev.)	I currently feel a relatively high level of pressure.
Emotional distress (rev.)	I am still clearly troubled by the current issue.
Relaxation	I feel calmer and more relaxed than at the beginning.
Hope	I feel that the current issue can gradually improve.
Self-regulation	I feel capable of coping with the current difficulty.

Appendix

Table 26: Immediate psychological-state items. The first two items are reverse-coded for scoring.

Dimension	Item
Understanding	The system understood the problem I was facing.
Acceptance	The system’s response made me feel accepted rather than judged.
Helpfulness	The system’s response was helpful to me.
Guidance clarity	The system’s guidance was clear; I knew what I could think or do next.
Continued-use	If I encounter a similar concern, I would be willing to use it again.
Satisfaction	Overall, I was satisfied with this experience.

Appendix

Table 27: Post-interaction subjective-experience items.

Figure 11: Detailed prompt card for the Planner module.

Figure 12: Detailed prompt card for the Writer module.

Figure 13: Detailed prompt card for the Hard Judge module.

Figure 14: Detailed prompt card for the Quality Judge module.

Figure 15: Detailed prompt card for the Stage Judge module.

Figure 16: Detailed prompt card for the JSON Repair module.

Figure 17: Detailed prompt card for the Q1 targeted rewrite instruction.

Figure 18: Detailed prompt card for the Q2 targeted rewrite instruction.

Figure 19: Detailed prompt card for the Q3 targeted rewrite instruction.

Figure 20: Detailed prompt card for the Q4 targeted rewrite instruction.

Figure 21: Detailed prompt card for the Q5 targeted rewrite instruction.

Figure 22: Detailed prompt card for the shared Q-targeted rewrite template.

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