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Use live and replay evaluation for different claims, then budget their noise separately. Replay a frozen answer or trace when you need to test a grader, parser, rubric, or regression script. Run the current model or agent live when the claim concerns what it would do now, including tool calls and a changing environment. A repeatable score is not automatically a valid score: a judge can be consistently wrong, and a live score can move because of serving or website changes rather than a model improvement.
This guide gives a practical noise budget for free model evaluations: identify the stage that can vary, control one stage at a time, and report enough run and version detail for someone else to interpret the result.
The decision in one minute
Choose replay for a fixed-candidate question: did a grader change, analysis-code change, or rubric change alter the score on these exact outputs? Choose live evaluation for a current-system question: does the present model or agent solve more tasks, call tools correctly, or complete a real workflow under today’s conditions?
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|---|---|---|---|
| “The new scorer changes scores on the same candidates.” | Replay frozen outputs or traces | Effect of the scorer, parser, rubric, or analysis code | How the model would generate those candidates again |
| “The current model is better on this benchmark.” | Repeated live generation on a controlled stack | Current model behavior, with measured run-to-run uncertainty | A claim about a different serving stack or future environment |
| “The agent completes this real multi-step task.” | Live end-to-end trials with tool and environment evidence | Task success under the tested environment and policy | That an old trajectory remains valid after the environment changes |
| “Our judge is reliable.” | Replay candidates and compare judge verdicts with executable or human-reviewed outcomes | Judge repeatability and agreement with the chosen reference | Correctness merely because verdicts are stable |
OpenAI’s evaluation guidance starts with the same discipline: define the objective, collect relevant data, specify metrics, compare evaluations, and continue evaluating after system changes. Its concise warning is worth keeping in view: Generative AI is variable. Models sometimes produce different output from the same input, which makes traditional software testing methods insufficient for AI architectures.
OpenAI Evaluation best practices.
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What live and replay mean
Live generation
In a live run, the evaluator sends the prompt or task to the current target model or agent and records the output produced during that trial. For a simple question-answer benchmark, that may be one request and one answer. For an agent, it can include planning steps, tool calls, a runner, intermediate observations, logs, and a final environment state. NVIDIA describes this split as dataset-driven evaluation, which produces one output per row online, versus task-driven evaluation, which records a task trial and its trace evidence. NVIDIA NeMo evaluation approaches.
Because a live trial obtains a new candidate, its score can combine token sampling, backend scheduling, execution behavior, tool failures, external data freshness, and judge variation. A difference between two live scores is therefore not automatically a model difference.
Replay
Replay scores a saved candidate, response, or trajectory again instead of asking the target to generate a new one. The candidate is held fixed while you rerun a grader, parser, rubric, metric implementation, or report. This makes replay the efficient control for regression testing: if the same frozen inputs produce a different score after a code change, the change is in the scoring path or its dependencies.
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Why the distinction matters
A replay result answers a question about a stored artifact. A live result answers a question about a current interaction. Treating them as interchangeable creates a validity error: you may report perfect scorer repeatability while knowing nothing about current generation, or report a live score change without knowing whether the website, backend, or judge changed.
The five-part noise budget
Write down the sources of variation before choosing a run count. The following budget separates stages that are often compressed into one benchmark number.
| Component | What can vary | Useful control or measurement | What the control does not establish |
|---|---|---|---|
| Generation | Sampled tokens, tool choices, refusals, or other decisions for the same input | Repeat live runs; record decoding settings and a seed when the interface exposes one | Temperature zero or a fixed seed guarantees identical results across serving stacks |
| Serving and execution | Parallel scheduling, floating-point behavior, backend version, hardware, timeouts, and configuration | Pin versions and configuration where possible; repeat on the same stack and report it | Pinning removes every hardware or implementation difference |
| Environment | Website state, tool availability, data freshness, permissions, network errors, and time-sensitive content | Preserve state and traces; define retry rules; classify infrastructure failures separately from valid task outcomes | A successful replay of old evidence makes the environment current |
| Grading | LLM-judge sampling, judge prompt or rubric, parser behavior, and scoring code | Freeze candidates and repeat the judge; compare with executable tests or human-reviewed cases | A consistent verdict is necessarily correct |
| Dataset and task mix | Sampling, stale tasks, exclusions, coverage gaps, and production mismatch | Version the data; document updates and removals; include cases that represent production use | Adding rows alone guarantees representative coverage |
Keep a separate outcome and uncertainty estimate for each stage. Combining all variation into one “noise” number hides which intervention actually changed the result.
Match the evaluation method to the claim
| Question you are asking | Recommended design | Evidence to preserve | Typical failure if you choose the wrong design |
|---|---|---|---|
| Did a grader or parser change alter historical scores? | Replay an immutable candidate set through old and new scoring code | Candidate IDs, raw outputs, grader version, rubric, parser logs, and per-case deltas | New live generations obscure whether the scorer or model caused the difference |
| Does a decoding or model change alter generation? | Repeated live runs with dataset and serving configuration held constant | Prompt and dataset version, model identifier, decoding settings, seed if available, run timestamps, and raw outputs | A single run is treated as the model’s stable capability |
| Does an agent complete a real workflow? | Live task-driven trials with trace and environment-state capture | Tool calls, observations, action sequence, final state, errors, retries, and environment timestamp | Replay of an old trace is presented as current task success |
| Is an LLM judge aligned with correctness? | Replay fixed candidates, repeat judge decisions, then compare against executable tests or human-reviewed references | Judge prompts, verdicts, rationale fields if used, reference outcomes, and disagreement cases | Low judge variance is mistaken for high validity |
A practical three-stage run ladder
Use this ladder when you are unsure how much rerunning is justified. It is an editorial operating pattern, not a standardized protocol.
- Replay fixed candidates first. Run the same outputs or traces through the scorer you intend to trust. This isolates grader, parser, and analysis behavior at low call cost.
- Repeat generation on one controlled stack. Keep the dataset, model or serving configuration, and grader versions constant. Repeat enough trials to estimate candidate variability for the claim you will make.
- Run live end to end when interaction is the claim. Let the current agent use its tools in the target environment, recording traces and final state. Keep environment failures distinct from valid task failures.
The OpenAI Cookbook’s Realtime Eval Guide illustrates a staged approach that uses repeatable inputs, replayed recorded inputs, and multi-turn evaluation. The staging principle is useful even when your system is not realtime: first make the input and scorer repeatable, then spend live calls on the uncertainty that remains.
How many times should you run an eval?
There is no universal run count. The answer depends on the metric, number of tasks, per-task variance, paired or unpaired design, smallest difference that matters, and whether you need an interval, a ranking, or a release gate. State the unit of analysis before calculating variance: a per-task binary outcome, an across-task mean, uncertainty in that mean, and disagreement between judge verdicts are different quantities.
For a fixed-candidate scorer check, one valid replay can be sufficient to reproduce a deterministic scoring path; repeat when the judge or another stochastic dependency is part of the path. For live generation, use repeated runs when a close comparison could change your decision, and report the run count and uncertainty rather than a lone point estimate. Pairing the same tasks across model versions usually removes some task-mix variation, but it does not remove generation, serving, environment, or judge noise.
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A version-one preprint by Aneesh Durai, Sarah Anthony, Connor White, Lida Guerrero, Juan Miguel Navarro Carranza, and Leandro Z. Agudelo, posted October 10, 2026 and explicitly marked not peer-reviewed, measured HumanEval+ and MBPP+ in two hosted model-and-verifier stacks. Treat these as study-specific observations, not constants for every evaluator. The Noise Floor of Agent Evaluation: Live Generation, Replay, and Rerun Budgets.
| Study measurement | Observed value | How to interpret it |
|---|---|---|
| Within-task variance in the primary stack during live evaluation | 0.0301 and 0.0142 for the two reported settings | Live candidate generation contributed measurable variation in that setup |
| Within-task variance under replay in the same primary stack | 0 and 0.0010 | Holding candidates fixed sharply reduced the measured variation there |
| Stochastic judge verdict variance | 0.0073 across 200 frozen MBPP+ candidates | Judge sampling alone can move scores even when candidates do not change |
| Judge at temperature zero | Observed verdict variation disappeared | Repeatability improved, but this did not prove the verdicts matched executed outcomes |
| Agreement with executed outcomes | Majority judgments disagreed on 23.5% of tasks in that experiment | A stable judge can still be systematically or inconsistently wrong relative to the reference outcome |
| Illustrative fixed-task difference of 0.02 | Authors reported point budgets of 3–8 runs per live arm and one under valid replay | Those budgets depend on the paper’s planning assumptions, metric, task sample, variance, and inference target; they are not a blanket recommendation |
Use the table to calibrate your questions, not to copy a number. If your model, API, verifier, task mix, or environment differs, estimate your own variance.
Does temperature zero make an eval deterministic?
No. Temperature zero and a fixed seed are controls, not cross-stack guarantees. Blackwell, Barry, and Cohn state that LLMs are stochastic and that not all models give deterministic answers even with temperature zero and a fixed random seed. Their 2025 preprint, version dated June 27, 2025, studies uncertainty in repeated benchmark scores. Towards Reproducible LLM Evaluation: Quantifying Uncertainty in LLM Benchmark Scores.
Record the exact decoding parameters and seed value when available, but still verify outputs empirically. Differences can arise from tokenizer or model revisions, floating-point kernels, batching, parallel scheduling, backend routing, tool timing, and hidden service changes. A seed can make one implementation repeatable while another implementation maps that seed differently.
A sensible determinism check
- Run the same prompt and candidate path multiple times on the same stack.
- Compare raw text, structured fields, tool calls, and final outcomes separately; two texts can differ while both pass the same executable test.
- Change only the stack or serving configuration in a second arm if you need to measure portability.
- Report any observed differences instead of declaring determinism from settings alone.
How to build a replay regression test
- Freeze the candidate set. Store raw responses, structured outputs, tool traces, and a stable case identifier. Do not regenerate candidates during a scorer regression.
- Version every scoring dependency. Record grader prompt or rubric, parser, executable tests, analysis code, dataset revision, and model used as a judge.
- Run the baseline scorer. Save per-case verdicts and aggregate metrics, not only the final mean.
- Apply one change. Update the rubric, parser, grader model, or analysis code while leaving candidates untouched.
- Replay and diff. Compare per-case outcomes, score deltas, parse failures, timeouts, and aggregate uncertainty. Investigate changed cases before accepting a new release gate.
- Check outcome validity. For code, execute tests where possible; for subjective tasks, maintain a human-reviewed reference sample. A stable replay score is evidence of repeatability, not proof of correctness.
Replay is especially powerful for testing graders because it prevents candidate drift from contaminating the comparison. It is also cheap enough to run in continuous integration, provided the grader and all stochastic dependencies are controlled or their uncertainty is explicitly measured.
How to run a live agent evaluation responsibly
- Define the task outcome. Specify what counts as success, partial completion, a valid failure, a timeout, and an infrastructure error before running trials.
- Capture the full trial. Preserve prompts, model and serving identifiers, tool requests and responses, intermediate observations, logs, action count, and final environment state.
- Pin what you can. Use fixed dataset and grader versions, stable tool images, known permissions, and documented configuration. Note every dependency you cannot pin.
- Timestamp the environment. Websites, inventories, prices, APIs, and other external data can change between runs. Include the test window and relevant state snapshot.
- Define retries before looking at results. A retry policy that changes after seeing failures can bias the estimate. Keep infrastructure retries separate from retries that give the agent another chance at a valid task.
- Repeat the tasks that matter. Use paired task sets when comparing systems and increase live repetitions when the observed difference is close to your decision threshold.
Microsoft’s Fara reproducibility note is a concrete example, not a universal policy: its described pipeline updated time-sensitive tasks, retried environment errors up to five times, did not retry complete but incorrect trajectories, and imposed a 100-action maximum. The note also says that pipeline predates the Fara-1.5 evaluation stack. If you borrow an idea, state your own rationale and limits rather than presenting those settings as standard. Microsoft Fara Eval Reproducibility.
How to separate model noise from judge noise
Use a factorial mindset: hold one stage fixed while varying the other. Start with a frozen candidate set and repeat the judge. That estimates judge and scoring-path variation without generation noise. Then hold the judge and candidate set constant while obtaining repeated live candidates. That estimates generation and serving variation. If the task uses tools, add a live environment arm rather than folding tool failures into model variance.
| Arm | Candidate | Judge | Main variation measured |
|---|---|---|---|
| A: scorer repeatability | Frozen | Repeated or changed as planned | Judge, parser, rubric, and analysis variation |
| B: generation repeatability | Repeated live | Frozen and deterministic where possible | Model generation plus serving and execution variation |
| C: end-to-end realism | Repeated live agent trials | Production-like scoring | Generation, tools, environment, and judge together |
Do not subtract one variance from another unless your statistical design supports that operation. Report the components as separate diagnostics, because the same aggregate score can arise from different combinations of candidate and judge noise.
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Environment drift, retries, and exclusions
External state turns a benchmark into an observational study of the environment as well as the model. A page can change, a tool can fail, a permission can expire, or a task can become impossible. Preserve the evidence needed to classify the event: request and response logs, timestamps, status codes, tool output, and the final state.
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- Infrastructure error: the runner, network, service, or tool failed before the agent could produce a valid task attempt. Apply the predeclared retry or exclusion rule and report the count.
- Valid task failure: the agent completed a trial but produced an incorrect, unsafe, or incomplete result. Do not silently retry it as if it were an outage.
- Stale or impossible task: the task’s premise no longer exists. Version the task set, document the change, and state whether the case was removed, updated, or scored as unavailable.
- Timeout or action-limit failure: the agent exceeded a declared operational limit. Keep it visible; changing the limit changes the claim.
A replay of yesterday’s trace can verify that your scorer still handles yesterday’s evidence. It cannot establish that today’s website, permissions, or data would yield the same trajectory.
Make the grader answerable to an outcome
LLM judges are useful for open-ended qualities, but their verdicts need an external anchor. For code or structured tasks, prefer executable tests, type checks, or known references where they genuinely measure the objective. For subjective work, create a human-reviewed calibration set and inspect disagreements rather than relying only on inter-judge agreement.
When a judge is stochastic, report its repeatability separately from its agreement with the reference. The Durai et al. experiment demonstrates why: setting temperature to zero removed observed verdict variation in its tested configuration, yet majority judgments still disagreed with executed outcomes on 23.5% of tasks. Stable disagreement is still disagreement.
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A reader should be able to tell what was run, what was held fixed, and what uncertainty remains. Include:
- Benchmark or task-set name, version, sampling method, and any updated, removed, stale, or impossible cases.
- Model identifier and serving configuration, including endpoint or backend version when available.
- Dataset and grader versions, rubric or judge prompt, parser, executable tests, and analysis-code revision.
- Whether each number came from live generation, replayed candidates, or replayed traces.
- Number of tasks and independent runs, plus the unit used for variance or uncertainty.
- Decoding settings, seed policy, temperature, maximum tokens or actions, and timeout policy.
- Retry, exclusion, and infrastructure-error rules, applied as written before inspecting outcomes.
- Environment date or snapshot, tool versions, permissions, and any external-state dependencies.
- Per-case outcomes and raw artifacts retained for audit, not just an aggregate score.
- An interval or other uncertainty summary when a comparison is close, instead of a single point estimate.
These details follow the reproducibility concerns in the Turing preprint, OpenAI’s evaluation guidance, and the live-agent documentation above. They turn a score into an interpretable measurement rather than an unexplained decimal.
Common mistakes and fixes
| Mistake | Why it misleads | Fix |
|---|---|---|
| One live run per model | A single sampled trajectory can look like a capability difference | Repeat live runs on the same task set and report uncertainty |
| Regenerating candidates while testing a grader | Candidate drift and grader changes become inseparable | Replay an immutable candidate set |
| Calling temperature zero deterministic | Serving stacks and implementations can still vary | Record settings, test repeatability, and qualify the claim |
| Retrying every failure | Retries can turn a real task failure into an artificially favorable score | Classify infrastructure errors and valid failures before the run |
| Dropping time-sensitive cases silently | Coverage and denominator change without disclosure | Version tasks and report every exclusion or update |
| Using judge agreement as correctness | Judges can share the same blind spot | Check executable or human-reviewed outcomes |
| Publishing only the mean | Readers cannot see run count, spread, or close-call uncertainty | Publish per-case results, run count, and an appropriate uncertainty measure |
| Replaying an old agent trace as current success | The environment and model may no longer behave the same way | Label it as replay evidence and run a current live trial for current-system claims |
A lightweight policy for free evaluation stacks
You do not need a paid evaluation platform to apply this method. A versioned dataset, immutable artifact store, runner logs, a scorer that emits per-case results, and a small report are enough to begin. Keep the policy explicit:
- Write the claim in one sentence and label it as candidate, model, or end-to-end system evidence.
- Choose replay, controlled live generation, or live task trials to match that claim.
- Freeze and version every stage that is not being measured.
- Run the replay arm before spending calls on live trials.
- Increase live repetitions only when the remaining uncertainty could change the decision.
- Validate the grader against an outcome reference and inspect disagreement cases.
- Publish run count, uncertainty, retries, exclusions, versions, and environment timing with the score.
This policy keeps cost proportional to the question. Replay gives you a low-cost, repeatable check of scoring and regressions; live evaluation supplies evidence about current generation and interaction. Neither replaces the other, and neither turns a stable number into proof of correctness without an outcome check.
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