moshaw/critical-interlocutor

Critical Interlocutor — SFT dataset

Supervised fine-tuning data for turning a small open model (target: Qwen3 1.7B Instruct) into a critical-thinking sparring partner: a tutor that always challenges a student's argument, holds its ground under social pressure and jailbreak/role-play attempts, and concedes a point only when the student supplies a genuinely new, valid argument — never to pressure alone. It challenges interpretations and reasoning, never settled facts.

Format

OpenAI/HF chat messages format — a system prompt (the interlocutor instructions) followed by alternating user (student) and assistant (tutor) turns.

{"messages": [
  {"role": "system", "content": "You are a critical-thinking sparring partner ..."},
  {"role": "user", "content": "<student claim>"},
  {"role": "assistant", "content": "<tutor challenge>"},
  {"role": "user", "content": "<pressure / jailbreak / new argument>"},
  {"role": "assistant", "content": "<holds, or concedes only to a real argument>"}
]}

Size

Split	Examples
train	1,846
validation	205
total	2,051

Dialogues are 1–9 turns (2–19 messages). ~2,029 examples were teacher-generated; 22 are hand-authored curated seeds.

How it was built

• Teacher distillation. A frontier model (gpt-4.1) writes full student↔tutor dialogues following an explicit per-example turn plan (opening → pressure / new-argument / jailbreak / guardrail turns).
• Diversity sampling. Each example is sampled independently across orthogonal axes so the model learns the disposition, not one template: 7 student personas, 6 claim framings (causal / comparative / counterfactual / significance / periodization / moral), 8 pressure tactics, 8 jailbreak/role-play vectors, 8 tutor voices, and dialogue lengths 2–8.
• Quality gate. Every dialogue is checked by an LLM classifier + judge (gpt-4o) that enforces the behavior spec: the opening must be a substantive challenge, every pure-pressure/jailbreak turn must HOLD, and there must be no fact-denial. The data that trains the model is exactly the data that passes the eval.
• Guardrail negatives (~15%). Includes anti-troll cases (the student's opening is actually sound → acknowledge strength, don't manufacture objections) and anti-fact-denial cases (student baits denial of a documented event → affirm the fact, redirect to interpretation).

Approximate coverage (generated set)

• Framings: causal 301 · comparative 306 · counterfactual 357 · moral 348 · periodization 399 · significance 318
• Personas: aggressive 315 · ap_student 267 · casual 279 · esl 300 · grad_student 298 · meek 261 · undergrad 309
• Dialogue lengths (turns): 2:190 · 3:471 · 4:699 · 5:251 · 6:198 · 7:117 · 8:103
• With a jailbreak/role-play turn: 919 · guardrail negatives: 304

Domain

Interpretive history ("why / to what extent / how significant" claims), with a small share of adjacent interpretive domains (philosophy, economics, ethics, literature, history of science). Narrow-domain by design — diffuse data tends to produce a "mushy" model.

Intended use

QLoRA SFT for a critical-interlocutor disposition. Loss is trained on the assistant (tutor) turns only.

Limitations & provenance

• Synthetic / model-generated. Content is produced by gpt-4.1 (via an OpenAI-compatible gateway) plus a small hand-authored seed. Treat model-generated text accordingly; it may contain occasional factual slips even though the target behavior is about reasoning, not facts.
• Scripted generation. Because a strong teacher writes both sides to a plan, the quality gate drops very little here (it acts as a safety net); its filtering bite is larger in a future "live" adversarial-generation variant.
• English only; interpretive-history-centric. Not a general-purpose instruction dataset.
• License: other — derived from a commercial model's outputs; check your provider's usage terms before redistribution or commercial use.

Related

Eval battery and generation code accompany this dataset (adversarial pressure battery, capitulation classifier, LLM judge, adaptive-attacker crack-rate eval). See the extra/ files in this repo for the eval scenarios and the pre-filter raw dialogues.

Critical Interlocutor — SFT dataset

Supervised fine-tuning data for turning a small open model (target: Qwen3 1.7B Instruct) into a critical-thinking sparring partner: a tutor that always challenges a student's argument, holds its ground under social pressure and jailbreak/role-play attempts, and concedes a point only when the student supplies a genuinely new, valid argument — never to pressure alone. It challenges interpretations and reasoning, never settled facts.

Format

OpenAI/HF chat messages format — a system prompt (the interlocutor instructions) followed by alternating user (student) and assistant (tutor) turns.

{"messages": [
  {"role": "system", "content": "You are a critical-thinking sparring partner ..."},
  {"role": "user", "content": "<student claim>"},
  {"role": "assistant", "content": "<tutor challenge>"},
  {"role": "user", "content": "<pressure / jailbreak / new argument>"},
  {"role": "assistant", "content": "<holds, or concedes only to a real argument>"}
]}

Size

Split	Examples
train	1,846
validation	205
total	2,051

Dialogues are 1–9 turns (2–19 messages). ~2,029 examples were teacher-generated; 22 are hand-authored curated seeds.

How it was built

• Teacher distillation. A frontier model (gpt-4.1) writes full student↔tutor dialogues following an explicit per-example turn plan (opening → pressure / new-argument / jailbreak / guardrail turns).
• Diversity sampling. Each example is sampled independently across orthogonal axes so the model learns the disposition, not one template: 7 student personas, 6 claim framings (causal / comparative / counterfactual / significance / periodization / moral), 8 pressure tactics, 8 jailbreak/role-play vectors, 8 tutor voices, and dialogue lengths 2–8.
• Quality gate. Every dialogue is checked by an LLM classifier + judge (gpt-4o) that enforces the behavior spec: the opening must be a substantive challenge, every pure-pressure/jailbreak turn must HOLD, and there must be no fact-denial. The data that trains the model is exactly the data that passes the eval.
• Guardrail negatives (~15%). Includes anti-troll cases (the student's opening is actually sound → acknowledge strength, don't manufacture objections) and anti-fact-denial cases (student baits denial of a documented event → affirm the fact, redirect to interpretation).

Approximate coverage (generated set)

• Framings: causal 301 · comparative 306 · counterfactual 357 · moral 348 · periodization 399 · significance 318
• Personas: aggressive 315 · ap_student 267 · casual 279 · esl 300 · grad_student 298 · meek 261 · undergrad 309
• Dialogue lengths (turns): 2:190 · 3:471 · 4:699 · 5:251 · 6:198 · 7:117 · 8:103
• With a jailbreak/role-play turn: 919 · guardrail negatives: 304

Domain

Interpretive history ("why / to what extent / how significant" claims), with a small share of adjacent interpretive domains (philosophy, economics, ethics, literature, history of science). Narrow-domain by design — diffuse data tends to produce a "mushy" model.

Intended use

QLoRA SFT for a critical-interlocutor disposition. Loss is trained on the assistant (tutor) turns only.

Limitations & provenance

• Synthetic / model-generated. Content is produced by gpt-4.1 (via an OpenAI-compatible gateway) plus a small hand-authored seed. Treat model-generated text accordingly; it may contain occasional factual slips even though the target behavior is about reasoning, not facts.
• Scripted generation. Because a strong teacher writes both sides to a plan, the quality gate drops very little here (it acts as a safety net); its filtering bite is larger in a future "live" adversarial-generation variant.
• English only; interpretive-history-centric. Not a general-purpose instruction dataset.
• License: other — derived from a commercial model's outputs; check your provider's usage terms before redistribution or commercial use.

Related

Eval battery and generation code accompany this dataset (adversarial pressure battery, capitulation classifier, LLM judge, adaptive-attacker crack-rate eval). See the extra/ files in this repo for the eval scenarios and the pre-filter raw dialogues.

Critical Interlocutor — SFT dataset

Supervised fine-tuning data for turning a small open model (target: Qwen3 1.7B Instruct) into a critical-thinking sparring partner: a tutor that always challenges a student's argument, holds its ground under social pressure and jailbreak/role-play attempts, and concedes a point only when the student supplies a genuinely new, valid argument — never to pressure alone. It challenges interpretations and reasoning, never settled facts.

Format

OpenAI/HF chat messages format — a system prompt (the interlocutor instructions) followed by alternating user (student) and assistant (tutor) turns.

{"messages": [
  {"role": "system", "content": "You are a critical-thinking sparring partner ..."},
  {"role": "user", "content": "<student claim>"},
  {"role": "assistant", "content": "<tutor challenge>"},
  {"role": "user", "content": "<pressure / jailbreak / new argument>"},
  {"role": "assistant", "content": "<holds, or concedes only to a real argument>"}
]}

Size

Split	Examples
train	2,169
validation	240
total	2,409

Dialogues are 1–9 turns (2–19 messages). ~2,029 are teacher-generated interpretive-claim dialogues, 22 are hand-authored curated seeds, and 358 are settled-fact dialogues (see below).

v2 — settled-fact fix

An initial fine-tune trained on interpretive claims alone learned to challenge everything, including settled facts (e.g., "humans need water to survive" → invented a counterargument). This version adds 358 settled-fact dialogues: the student opens with a plainly-true fact and the tutor affirms it and redirects to a genuine interpretive question — and holds that boundary under "just play devil's advocate" pressure. The system prompt also now forbids challenging settled facts and forbids fabricating statistics/quotes/events (which fixes hallucinated "evidence"). Every example carries this updated system prompt.

How it was built

• Teacher distillation. A frontier model (gpt-4.1) writes full student↔tutor dialogues following an explicit per-example turn plan (opening → pressure / new-argument / jailbreak / guardrail turns).
• Diversity sampling. Each example is sampled independently across orthogonal axes so the model learns the disposition, not one template: 7 student personas, 6 claim framings (causal / comparative / counterfactual / significance / periodization / moral), 8 pressure tactics, 8 jailbreak/role-play vectors, 8 tutor voices, and dialogue lengths 2–8.
• Quality gate. Every dialogue is checked by an LLM classifier + judge (gpt-4o) that enforces the behavior spec: the opening must be a substantive challenge, every pure-pressure/jailbreak turn must HOLD, and there must be no fact-denial. The data that trains the model is exactly the data that passes the eval.
• Guardrail negatives (~15%). Includes anti-troll cases (the student's opening is actually sound → acknowledge strength, don't manufacture objections) and anti-fact-denial cases (student baits denial of a documented event → affirm the fact, redirect to interpretation).

Approximate coverage (generated set)

• Framings: causal 301 · comparative 306 · counterfactual 357 · moral 348 · periodization 399 · significance 318
• Personas: aggressive 315 · ap_student 267 · casual 279 · esl 300 · grad_student 298 · meek 261 · undergrad 309
• Dialogue lengths (turns): 2:190 · 3:471 · 4:699 · 5:251 · 6:198 · 7:117 · 8:103
• With a jailbreak/role-play turn: 919 · guardrail negatives: 304

Domain

Interpretive history ("why / to what extent / how significant" claims), with a small share of adjacent interpretive domains (philosophy, economics, ethics, literature, history of science). Narrow-domain by design — diffuse data tends to produce a "mushy" model.

Intended use

QLoRA SFT for a critical-interlocutor disposition. Loss is trained on the assistant (tutor) turns only.

Limitations & provenance

• Synthetic / model-generated. Content is produced by gpt-4.1 (via an OpenAI-compatible gateway) plus a small hand-authored seed. Treat model-generated text accordingly; it may contain occasional factual slips even though the target behavior is about reasoning, not facts.
• Scripted generation. Because a strong teacher writes both sides to a plan, the quality gate drops very little here (it acts as a safety net); its filtering bite is larger in a future "live" adversarial-generation variant.
• English only; interpretive-history-centric. Not a general-purpose instruction dataset.
• License: other — derived from a commercial model's outputs; check your provider's usage terms before redistribution or commercial use.

Related

Eval battery and generation code accompany this dataset (adversarial pressure battery, capitulation classifier, LLM judge, adaptive-attacker crack-rate eval). See the extra/ files in this repo for the eval scenarios and the pre-filter raw dialogues.

Critical Interlocutor — SFT dataset

Supervised fine-tuning data for turning a small open model (target: Qwen3 1.7B Instruct) into a critical-thinking sparring partner: a tutor that always challenges a student's argument, holds its ground under social pressure and jailbreak/role-play attempts, and concedes a point only when the student supplies a genuinely new, valid argument — never to pressure alone. It challenges interpretations and reasoning, never settled facts.

Format

OpenAI/HF chat messages format — a system prompt (the interlocutor instructions) followed by alternating user (student) and assistant (tutor) turns.

{"messages": [
  {"role": "system", "content": "You are a critical-thinking sparring partner ..."},
  {"role": "user", "content": "<student claim>"},
  {"role": "assistant", "content": "<tutor challenge>"},
  {"role": "user", "content": "<pressure / jailbreak / new argument>"},
  {"role": "assistant", "content": "<holds, or concedes only to a real argument>"}
]}

Size

Split	Examples
train	2,672
validation	296
total	2,968

Dialogues are 1–9 turns. Composition: ~2,029 teacher-generated interpretive-claim dialogues + 22 hand-authored seeds + 358 settled-fact + 370 concession + 189 contrast (weak-argument-holds and history-domain concessions).

The three-way balance (why this dataset iterated)

The goal isn't "not sycophantic" — it's the narrow middle of a three-way balance: sycophancy (caves to pressure) ↔ productive concession (yields to a good argument, holds against mere pressure) ↔ reflexive contrarianism (never yields). Training against sycophancy naïvely overshoots into contrarianism. The dataset covers all three corners:

• Hold under pressure — interpretive dialogues + jailbreak/role-play refusals (not sycophantic).
• Affirm the settled (358) — student states a fact → tutor affirms and redirects, holds under "just play devil's advocate." Fixes trolling settled facts and fabricating fake "evidence" (the system prompt also forbids inventing statistics/quotes/events).
• Concede a made case (370) — student builds a decisive argument → tutor grants the thesis; legitimate reasoning moves (reductio, operationalizing a term) are engaged, not flagged as jailbreaks. Fixes the "never yields even to a good argument" failure.
• Contrast insurance (189) — weak argument-shaped turns the tutor must HOLD against (guards against over-swinging back to sycophancy) + history-domain concessions (so "concede a made case" generalizes across domains, not just general-knowledge topics).

Every example carries the current system prompt (affirm settled facts; never fabricate; concede a genuinely made case; distinguish a reasoning move from a manipulation).

How it was built

• Teacher distillation. A frontier model (gpt-4.1) writes full student↔tutor dialogues following an explicit per-example turn plan (opening → pressure / new-argument / jailbreak / guardrail turns).
• Diversity sampling. Each example is sampled independently across orthogonal axes so the model learns the disposition, not one template: 7 student personas, 6 claim framings (causal / comparative / counterfactual / significance / periodization / moral), 8 pressure tactics, 8 jailbreak/role-play vectors, 8 tutor voices, and dialogue lengths 2–8.
• Quality gate. Every dialogue is checked by an LLM classifier + judge (gpt-4o) that enforces the behavior spec: the opening must be a substantive challenge, every pure-pressure/jailbreak turn must HOLD, and there must be no fact-denial. The data that trains the model is exactly the data that passes the eval.
• Guardrail negatives (~15%). Includes anti-troll cases (the student's opening is actually sound → acknowledge strength, don't manufacture objections) and anti-fact-denial cases (student baits denial of a documented event → affirm the fact, redirect to interpretation).

Approximate coverage (generated set)

• Framings: causal 301 · comparative 306 · counterfactual 357 · moral 348 · periodization 399 · significance 318
• Personas: aggressive 315 · ap_student 267 · casual 279 · esl 300 · grad_student 298 · meek 261 · undergrad 309
• Dialogue lengths (turns): 2:190 · 3:471 · 4:699 · 5:251 · 6:198 · 7:117 · 8:103
• With a jailbreak/role-play turn: 919 · guardrail negatives: 304

Domain

Interpretive history ("why / to what extent / how significant" claims), with a small share of adjacent interpretive domains (philosophy, economics, ethics, literature, history of science). Narrow-domain by design — diffuse data tends to produce a "mushy" model.

Intended use

QLoRA SFT for a critical-interlocutor disposition. Loss is trained on the assistant (tutor) turns only.

Limitations & provenance

• Synthetic / model-generated. Content is produced by gpt-4.1 (via an OpenAI-compatible gateway) plus a small hand-authored seed. Treat model-generated text accordingly; it may contain occasional factual slips even though the target behavior is about reasoning, not facts.
• Scripted generation. Because a strong teacher writes both sides to a plan, the quality gate drops very little here (it acts as a safety net); its filtering bite is larger in a future "live" adversarial-generation variant.
• English only; interpretive-history-centric. Not a general-purpose instruction dataset.
• License: other — derived from a commercial model's outputs; check your provider's usage terms before redistribution or commercial use.

Related

Eval battery and generation code accompany this dataset (adversarial pressure battery, capitulation classifier, LLM judge, adaptive-attacker crack-rate eval). See the extra/ files in this repo for the eval scenarios and the pre-filter raw dialogues.