The Four Stages of GPT Training

Pre-training, supervised fine-tuning, reward modeling and RL: stage goals, inputs, algorithms and failure modes; unverifiable hyperparameters are marked unknown.

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Start with this prompt

Explains pre-training, supervised fine-tuning, reward modeling and reinforcement learning from public sources, making clear the typical pipeline is not any company's internal recipe.

Engineer Training Handout

Switches the depth axis to engineering teaching; the deliverable adds loss signals, pseudocode and exercises.

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Research the four-stage training framework of GPT-class large language models as of July 13, 2026. Shared research protocol: organize the work around the four stages of pre-training, supervised fine-tuning, reward modeling and reinforcement learning; explain goals, inputs, outputs, stage dependencies, algorithms, evaluation and failure modes, without claiming to reconstruct any lab's unpublished internal recipe. Prioritize original papers, institutional technical reports and system cards, plus verifiable public code, dataset documentation and model cards; use surveys only to locate evidence. Cite and cross-check algorithm names, data and model versions, dates and causal claims item by item; mark hyperparameters, data mixtures or implementation details that cannot be verified as unknown instead of filling gaps with speculation. The source cutoff is July 13, 2026; distinguish public facts, evidence-supported interpretations and future directions. The general deliverable must cover the four-stage pipeline, data flow between stages, evidence strength, evaluation conditions, model versions, reproducibility scope, extrapolation limits, safety boundaries and references. Task module: Explain the problems the four stages solve, how they connect, their benefits and limits; deliver a research report with a stage comparison, a timeline of key papers, data and objective-function diagrams, an evaluation matrix and evidence gaps. Do not provide operational guidance for bypassing safety controls, and do not extrapolate an institution's self-reported claims or a single evaluation into general conclusions.

Training-Stage Reproduction Experiment

Switches the task to a small checkable experiment that uses public data and models to compare stage increments, failure conditions and reproduction differences.

Try Deep Research

Research the four-stage training framework of GPT-class large language models as of July 13, 2026. Shared research protocol: organize the work around the four stages of pre-training, supervised fine-tuning, reward modeling and reinforcement learning; explain goals, inputs, outputs, stage dependencies, algorithms, evaluation and failure modes, without claiming to reconstruct any lab's unpublished internal recipe. Prioritize original papers, institutional technical reports and system cards, plus verifiable public code, dataset documentation and model cards; use surveys only to locate evidence. Cite and cross-check algorithm names, data and model versions, dates and causal claims item by item; mark hyperparameters, data mixtures or implementation details that cannot be verified as unknown instead of filling gaps with speculation. The source cutoff is July 13, 2026; distinguish public facts, evidence-supported interpretations and future directions. The general deliverable must cover the four-stage pipeline, data flow between stages, evidence strength, evaluation conditions, model versions, reproducibility scope, extrapolation limits, safety boundaries and references. Task module: Explain the problems the four stages solve, how they connect, their benefits and limits; deliver a research report with a stage comparison, a timeline of key papers, data and objective-function diagrams, an evaluation matrix and evidence gaps. Do not provide operational guidance for bypassing safety controls, and do not extrapolate an institution's self-reported claims or a single evaluation into general conclusions.

RLHF Evidence Audit

Switches the research axis to an RLHF evidence audit, comparing methods, baselines, metrics and extrapolation limits.

Try Deep Research

Research the four-stage training framework of GPT-class large language models as of July 13, 2026. Shared research protocol: organize the work around the four stages of pre-training, supervised fine-tuning, reward modeling and reinforcement learning; explain goals, inputs, outputs, stage dependencies, algorithms, evaluation and failure modes, without claiming to reconstruct any lab's unpublished internal recipe. Prioritize original papers, institutional technical reports and system cards, plus verifiable public code, dataset documentation and model cards; use surveys only to locate evidence. Cite and cross-check algorithm names, data and model versions, dates and causal claims item by item; mark hyperparameters, data mixtures or implementation details that cannot be verified as unknown instead of filling gaps with speculation. The source cutoff is July 13, 2026; distinguish public facts, evidence-supported interpretations and future directions. The general deliverable must cover the four-stage pipeline, data flow between stages, evidence strength, evaluation conditions, model versions, reproducibility scope, extrapolation limits, safety boundaries and references. Task module: Explain the problems the four stages solve, how they connect, their benefits and limits; deliver a research report with a stage comparison, a timeline of key papers, data and objective-function diagrams, an evaluation matrix and evidence gaps. Do not provide operational guidance for bypassing safety controls, and do not extrapolate an institution's self-reported claims or a single evaluation into general conclusions.