Paper, researchers, and primary source
Major lab research / alignment_and_safety
InstructGPT combines demonstrations, preference comparisons, a learned reward model, and reinforcement learning to make language models follow user instructions more reliably.
Contribution 1
Established a practical supervised-fine-tuning plus RLHF pipeline.
Contribution 2
Showed a smaller aligned model could be preferred over a much larger base model.
Contribution 3
Measured helpfulness and safety while documenting remaining failure modes.
Research context
alignment_and_safety / 2022
Training language models to follow instructions with human feedback places instructgpt inside the broader alignment and safety discussion at OpenAI, with rlhf supplying a second analytical lens. Read together, the source records three advances: Established a practical supervised-fine-tuning plus RLHF pipeline; Showed a smaller aligned model could be preferred over a much larger base model; and Measured helpfulness and safety while documenting remaining failure modes. Keeping those moves together prevents instruction following from being detached from its evidence. For an implementation review, the relevant consequence is that post-training can change perceived usefulness more than scale alone, so base and instruction-tuned models should be evaluated separately.
Methods and evidence reading
1 cataloged method notes
Method 1
The experimental design in Training language models to follow instructions with human feedback tests established a practical supervised-fine-tuning plus rlhf pipeline and showed a smaller aligned model could be preferred over a much larger base model against the paper's documented baselines, datasets, model variants, or systems workloads.
How to read the evidence
The evidentiary value of Training language models to follow instructions with human feedback comes from the relationship among its reported moves. Two entries define the method-level claim: Established a practical supervised-fine-tuning plus RLHF pipeline; and Showed a smaller aligned model could be preferred over a much larger base model. The cataloged result is: Measured helpfulness and safety while documenting remaining failure modes. On that basis, post-training can change perceived usefulness more than scale alone, so base and instruction-tuned models should be evaluated separately. The catalog nevertheless records that the empirical reach of Training language models to follow instructions with human feedback stops at rater instructions, prompt sampling, model revisions, construct validity, selected threat model, and evaluator models; broader rlhf use therefore requires fresh measurements. Reproduction work should separate genuine instructgpt transfer from behavior caused by a changed rlhf setup. Rechecking Training language models to follow instructions with human feedback calls for an explicit instructgpt baseline, controlled rlhf changes, and a trace of cases that challenge Showed a smaller aligned model could be preferred over a much larger base model under the new setup.
Findings in the source record
1 paper-specific findings
- The reported evidence in Training language models to follow instructions with human feedback supports measured helpfulness and safety while documenting remaining failure modes.
Practical implication for AI builders
OpenAI / 2022
Proposed BrokenGPT application
Research blueprint / proposed status
Proposed route placement / instructgpt
For a proposed BrokenGPT experiment based on Training language models to follow instructions with human feedback, label base versus instruction-tuned endpoints clearly and collect consented preference data against a fixed quality rubric for future routing evaluations. Keep the instructgpt path isolated, versioned, and attributable to this research record.
Proposed acceptance test / rlhf
Validate the proposed instructgpt route against the paper's reported outcome: Measured helpfulness and safety while documenting remaining failure modes. Assess the proposed Training language models to follow instructions with human feedback route through adversarial coverage, refusal precision, and helpful-answer retention, and treat rlhf failures as their own instructgpt decision input.
Proposed decision boundary / instruction following
Balance usefulness, oversight burden, and residual risk before promoting the proposed instruction following design. Because for a later instructgpt implementation, adversarial adaptation, deployment drift, unsampled behaviors, language coverage, and judge bias define unresolved boundaries that require direct observation, adoption remains conditional on replay under BrokenGPT's selected model, runtime, and policy configuration.
Limitations, verification, and source
Boundaries recorded with the paper
Limitations
- The empirical reach of Training language models to follow instructions with human feedback stops at rater instructions, prompt sampling, model revisions, construct validity, selected threat model, and evaluator models; broader rlhf use therefore requires fresh measurements.
- For a later instructgpt implementation, adversarial adaptation, deployment drift, unsampled behaviors, language coverage, and judge bias define unresolved boundaries that require direct observation.
PRIMARY SOURCES
- 01Training language models to follow instructions with human feedback
OpenAI — Primary primary arXiv paper / 4 March 2022 / Long Ouyang, Jeff Wu, Xu Jiang, and 17 more