Epistemic High-Value Conversation Pipeline
Executive summary
This proposal introduces an opt-in pipeline to convert a small fraction of human-AI conversations into high-quality post-training signal.
The core thesis is simple:
Do not use more data. Use better supervision.
The proposal is based on the observation that not all conversations are equally useful for model improvement. A small subset of interactions is disproportionately valuable because it contains:
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a difficult prompt,
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an imperfect initial answer,
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a strong user critique or reframing,
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a corrected and epistemically better revised answer.
These interactions are more useful than large volumes of socially pleasant but cognitively weak chat.
The pilot would not only attempt to improve model behavior. It would also build infrastructure for learning, in a controlled way, which human-AI interactions should become training signal at all.
Problem
Post-training based on large volumes of raw conversation tends to mix useful signal with:
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sycophancy,
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shallow agreement,
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verbosity without epistemic gain,
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user framing errors,
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weak corrective supervision.
A model can easily become more pleasant without becoming more rigorous.
The most valuable conversations are often those in which the model is challenged, corrected, and improved.
Hypothesis
A small set of conversations that are:
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explicitly consented,
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privacy-sanitized,
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stratified,
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epistemically scored,
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human-audited,
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and distilled into reusable training units,
can produce better post-training signal than indiscriminate use of raw chat logs.
Project goal
Build a pipeline that can:
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identify high-value human-AI conversations,
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distinguish correct revision from capitulation to user pressure,
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extract reusable training units,
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improve rigor while reducing sycophancy,
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measure whether gains transfer outside the source domain.
Design principles
1. Users do not self-declare as “high value”
Users only opt in to the program.
Value is determined later by the pipeline.
2. Never train on identity
The system should not learn who said something.
It should learn which interaction pattern improved reasoning.
3. Never train on raw chat directly
Models should train on distilled training units, not full raw conversations.
4. Negative examples must not be used as positive SFT targets
Capitulation examples should only appear in:
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preference ranking,
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contrastive critique formats,
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or evaluation sets,
never as positive supervised targets.
5. Every critical component must be empirically calibratable
Scoring weights, judge models, thresholds, and revision detectors must be treated as empirical objects, not declared truths.
Pilot scope
A controlled pilot should be small but sufficient to test the hypothesis.
Suggested initial scope:
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opt-in users only,
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two languages,
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three initial domains:
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technical reasoning,
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critical analysis,
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iterative correction/synthesis.
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The pilot should answer four questions:
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Can epistemic scoring be calibrated reliably?
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Can a judge-model be validated against human gold sets?
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Can the system distinguish correct revision from capitulation?
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Do gains transfer out of domain?
Pipeline
The pipeline is:
opt-in → eligibility → sanitization → segmentation → hybrid scoring → revision/capitulation detection → distillation → corpus building → human audit → training → eval gate
Pipeline stages
1. Consent and eligibility
Only conversations from users who explicitly opt in are eligible.
Consent is an entry condition, not a value signal.
2. Sanitization
Remove or abstract:
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personal identifiers,
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health and financial details,
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company-sensitive information,
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unnecessary biographical detail.
The aim is to preserve the reasoning pattern, not the personal story.
3. Segmentation
Break the conversation into micro-trajectories such as:
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hard prompt,
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initial answer,
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user critique,
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revised answer,
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final synthesis.
4. Stratified sampling
Sampling should be stratified by:
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language,
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domain,
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task type,
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conversation structure,
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presence of critique,
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presence of revision.
This reduces selection bias.
5. Epistemic scoring
Each training unit candidate receives a score:
Q(u) = \sum_i w_i x_i - \sum_k v_k p_k
Where Q(u) is training-unit quality, x_i are positive dimensions, p_k are penalties, and the weights are bootstrap coefficients to be calibrated empirically.
Positive dimensions include:
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factuality,
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reasoning density,
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anti-sycophancy,
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uncertainty calibration,
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structural transferability,
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novelty,
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safety,
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utility.
Penalties include:
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social capitulation,
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verbosity,
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echoing user framing,
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pseudo-depth.
6. Response-level epistemic quality
The revision detector uses a separate function:
E(r) = \sum_j \alpha_j z_j(r) - \sum_k \beta_k p_k(r)
Where E(r) is response-level epistemic quality.
This must be kept separate from Q(u):
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E(r) evaluates a single response.
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Q(u) evaluates whether a whole interaction unit is valuable as training data.
7. Judge-model architecture
The judge is not final authority. It is a validated triage component with four layers:
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primary judge model,
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ensemble of judges,
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human gold set,
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challenger audit.
A judge-model must demonstrate:
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acceptable correlation with the human gold set,
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inter-prompt stability,
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low sensitivity to superficial style,
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good discrimination between correct revision and capitulation.
8. Correct revision vs capitulation
For a sequence:
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R1 = initial response
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Ucrit = user critique
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R2 = revised response
Define:
Rev = E(R2) - E(R1)
Cap = Align(Ucrit, R2) - Align(Ucrit, R1)
Interpretation:
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Correct revision: Rev > 0, without factual or logical degradation.
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Probable capitulation: Cap > 0 and Rev <= 0, or factuality/consistency falls.
9. Human gold set
Let:
G = |GoldSet_{human}|
G is an explicit pilot variable.
The gold set is used to:
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calibrate E(r),
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validate the judge-model,
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calibrate weights and thresholds,
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test revision vs capitulation.
G should not be fixed arbitrarily. It should grow until calibration becomes stable.
The gold set should be stratified and include:
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correct revision,
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neutral revision,
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capitulation,
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strong factual answers,
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elegant but wrong answers,
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over-agreeable answers,
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properly calibrated answers.
10. Transferability
Transferability has two levels.
T1: structural ex ante transferability
A proxy based on whether the pattern looks reusable across contexts.
T2: empirical ex post transferability
T_{emp} = \Delta performance_{OOD}
Where OOD means out-of-domain performance after training.
Real transferability is not assumed. It is measured.
11. Thresholds
All thresholds are provisional bootstrap parameters:
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θ_Q
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θ_J
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θ_Rev
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θ_Cap
They must be calibrated empirically using:
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ROC,
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precision-recall,
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false-positive cost,
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false-negative cost,
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human audit capacity.
12. Distillation
Approved interactions are distilled into reusable training units that preserve:
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the problem pattern,
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the error pattern,
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the correction pattern,
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the final answer pattern.
13. Corpora
The pipeline should generate:
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SFT-HighRigor
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Preference-AntiSycophancy
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Critique-Revision
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Calibration-Humility
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Capitulation-Negative
Capitulation-Negative must be used only in:
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preference pairs,
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contrastive critique,
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or evals,
never as positive SFT targets.
14. Human audit
Each corpus batch should undergo:
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random audit,
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premium-sample audit,
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full review of high-conflict items.
At least two human reviewers per audited item.
A third reviewer resolves major disagreement.
15. Eval gate
The pilot must test:
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factuality,
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anti-sycophancy,
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anti-capitulation,
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revision quality,
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uncertainty calibration,
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verbosity control,
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out-of-domain transfer.
A batch should be blocked if gains in one area produce meaningful regression in another.
Success criteria
The pilot is successful if it improves:
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factual reliability,
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revision after valid critique,
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resistance to sycophancy,
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resistance to capitulation,
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uncertainty calibration,
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out-of-domain performance.
It should also demonstrate that:
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scoring can be calibrated,
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the gold set is sufficient for stable calibration,
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judge-model drift can be controlled,
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negative contrastive data does not contaminate SFT.
Final thesis
A small subset of human-AI conversations, when properly consented, sanitized, sampled, scored, audited, and distilled, can provide post-training signal that is more valuable than large volumes of undifferentiated raw chat.
In one sentence:
The pilot is not only for training a better model; it is for learning, with operational rigor, which human interactions deserve to become training data in the first place.