fix(experimentalist): explain and refuse an evaluation that produced no objective metric by callingmedic911 · Pull Request #1357 · NVIDIA-NeMo/nemo-platform

callingmedic911 · 2026-08-17T21:50:49Z

Summary

A Harbor trial that finishes without writing /logs/verifier/reward.txt surfaced only as reward n/a, and the run continued. It now explains itself, and a baseline that cannot be measured ends the run instead of optimizing against nothing.

The reward was not lost in one place. Harbor detects the missing file and raises RewardFileNotFoundError; the trial adapter maps that to status="failed"; aggregate_results correctly excludes failed trials and returns {}; and the reporter renders the absent key as n/a. Each layer behaves correctly on its own, and the explanation is discarded between them.

Related Issue

Fixes the "reward n/a with no explanation" report: no warning that the verifier produced no reward, an Experimentalist run that proceeds anyway, and no documented /logs/verifier/reward.txt contract in the task authoring guide.

Changes

73 lines of source across 5 files, plus tests.

Diagnose and report (components/models.py, context.py, reporting.py): missing_objective_reason recovers, from the trials, why an aggregate omits an objective. ExperimentContext.evaluate passes it to the reporter, which prints it under the n/a line. For a Harbor verifier that wrote no reward file, RewardFileNotFoundError was already sitting unread in TrialResult.error. The helper names only targets and trial statuses, so no evaluator's vocabulary leaks into the metric contract.
Refuse an unmeasurable baseline (strategies/evolutionary.py): _record_baseline_validation raises rather than recording a baseline with no objective metric. The runner already marks the run failed and re-raises.
Document the contract (HarborDataset docstring, 8 lines): that docstring is the task authoring guide — EvalAuthor hands it to the model verbatim as context["dataset_documentation"] via doc(type(dataset), inline_depth=1). It documented reward.json thoroughly and never mentioned reward.txt or said what happens when a verifier writes nothing. reward.txt is now a second entry in the existing "must write" list, followed by the rule: writing one is mandatory, exiting 0 without either scores nothing rather than zero, and every agent outcome including failure must be scored.

Earlier revisions of this branch carried a tools/reward_pipeline_inspector.py used to locate which layer dropped the reward. It has been removed: the question is answered, the behaviour is pinned by tests, and keeping it meant maintaining a second description of the pipeline that could drift from the code.

Why the fix is not in `aggregate_results` or the terminator

aggregate_results already raises on a partial loss (ValueError: Inconsistent metrics across trials) and permits a total loss, because an all-empty key set is self-consistent. That asymmetry is defensible: partial loss makes the mean arithmetically meaningless, while total loss is simply an absence of data, and the loop is designed to survive a round in which every candidate crashed. Raising there would turn a survivable round into a run-ending error.

ConvergenceTerminator cannot own it either. Its len(rounds) < min_rounds_before_stopping guard is a warm-up gate, so "nothing scored" is indistinguishable from "not enough scored generations yet"; splitting them would add a branch that only fires after the expensive work is already paid for.

Two findings worth noting for review, both out of scope here:

reward_scalar in reporting.py has no caller anywhere in the repository. It is the source of the "missing reward becomes 0.0" reading, but nothing runs it. Ranking instead goes through pareto_objectives, which projects an unmeasured candidate to {}; _dominates then returns False against every peer, so one Pareto front held the whole population and ranking degraded to a no-op. Deleting the dead function is worth doing separately.
aggregate_results averages over completed trials only, so a candidate completing 1 of 4 tasks reports the surviving trial's reward as its score — plausible, comparable, and unquestioned by any later gate. Unlike an n/a, there is nothing on screen to notice.

Type of Change

Code change (feature, bug fix, or refactor)
Code change with documentation updates
Documentation only
Contributor tooling or automation
CI, build, or test infrastructure

Quality Gates

Tests added or updated for changed behavior
Existing tests cover changed behavior — justification:
Tests not applicable — justification:
Documentation updated for user-visible behavior
Documentation not applicable — justification:

Three tests, favouring coverage over count: one parametrised test in test_metric_contract.py covering every branch of missing_objective_reason; one added to test_reporting.py for the explained n/a; one in test_loop_helpers.py asserting the baseline guard refuses an unmeasurable baseline without recording it and still records a measured one.

Verification

Pull request title follows the repository's Conventional Commit format
Every commit includes an appropriate Signed-off-by: trailer
uv run pre-commit run -a passes, or any blocked checks are identified below
Targeted tests pass, or tests are marked not applicable above
No secrets, API keys, or credentials are included

Targeted validation:

uv run --frozen pytest plugins/nemo-experimentalist/tests plugins/nemo-eval-author/tests -q — 998 passed, 44 skipped.
uv run ruff check and uv run ruff format --check repo-wide — all checks passed.
uv run --frozen ty check plugins/nemo-experimentalist — 53 diagnostics, identical to the pre-change tree; no new type errors.
End-to-end check driving ExperimentContext.evaluate and _record_baseline_validation over real Harbor result.json shapes read through the production adapter. The raised shape and the silent shape each print their own explanation and then raise; the control case records reward 0.400 and continues unchanged. Output attached to the agent run.
Confirmed the contract text reaches the model: doc(HarborDataset, inline_depth=1) contains it.

Full pre-commit run -a was not run: it regenerates the OpenAPI spec and Helm docs, which this change does not touch.

Summary by CodeRabbit

Improvements
- Improved diagnostics for missing objective metrics, including trial status, failure details, and absent reward data.
- Baseline validation now rejects unmeasurable results while preserving valid measurements.
- Candidate evaluation reports can include clear failure reasons.
Bug Fixes
- Clarified reward-file requirements, supported formats, and failure behavior.
- Improved handling and reporting of missing or incomplete reward data, including genuine zero scores.
Tools
- Added a reward pipeline inspection tool for investigating verifier outcomes and reward flow.

…erifier rewards A Harbor trial that writes no /logs/verifier/reward.txt surfaces only as 'reward n/a', and the run continues. This inspector prints the state at every layer the reward passes through — the trial directory, trials_from_job_dir, aggregate_results, reward_scalar/RunReporter, and has_metric_dimensions — so the layer that drops the value is visible. It runs against synthetic trial shapes or a real Harbor job directory. Read-only and not imported by the plugin. Signed-off-by: Cursor Agent <cursoragent@cursor.com> Co-authored-by: Aditya Pandey <aditya@autospace.co>

…d inspector reward_scalar looks like the loop's scalar reward but has no caller in the loop. Label it as such, and print pareto_objectives alongside has_metric_dimensions so the layer that actually decides comparability is the one on screen. Signed-off-by: Cursor Agent <cursoragent@cursor.com> Co-authored-by: Aditya Pandey <aditya@autospace.co>

… inspector aggregate_results averages over completed trials only, so a candidate that completes 1 of 4 tasks reports the surviving trial's reward as its score. That number is plausible and comparable, so no later gate questions it. The scenario puts the completed/total count next to the aggregate that was derived from it. Signed-off-by: Cursor Agent <cursoragent@cursor.com> Co-authored-by: Aditya Pandey <aditya@autospace.co>

…printing bare n/a An evaluation that produces no objective metric rendered as 'reward n/a', which reads as a score of nothing rather than a measurement that never happened. The evidence for which one occurred lives on the trials -- their status and, for a Harbor verifier that wrote no reward file, a RewardFileNotFoundError already recorded in TrialResult.error -- and was discarded once a caller held only the aggregate. missing_objective_reason recovers it while the trials are still in hand, and ExperimentContext.evaluate passes it to the reporter, which now prints the explanation under the n/a line. The helper names only targets and trial statuses, so no evaluator's vocabulary leaks into the metric contract. Signed-off-by: Cursor Agent <cursoragent@cursor.com> Co-authored-by: Aditya Pandey <aditya@autospace.co>

…tive metric The baseline's validation reward is the one measurement every later comparison is against. When it is absent the loop did not notice: pareto_objectives projects an unmeasured candidate to {}, _dominates then returns False against every peer, so one Pareto front held the whole population and ranking became a no-op. The terminator could not stop either, because its warm-up guard reads 'nothing scored' as 'not enough scored generations yet'. The run therefore spent every round on analysis, proposals, and builds it could not rank, and finished with no winner. _record_baseline_validation now refuses to record such a baseline and raises. The runner already marks the run failed and re-raises, so the operator gets the reason at the cost of one evaluation rather than max_rounds rounds. Resume is unaffected: the check only sees a baseline this round actually measured. Signed-off-by: Cursor Agent <cursoragent@cursor.com> Co-authored-by: Aditya Pandey <aditya@autospace.co>

…horing guide The HarborDataset docstring is the task authoring guide: EvalAuthor hands it to the model verbatim as context['dataset_documentation']. It documented reward.json thoroughly and never said what happens when a verifier writes nothing, and it mentioned reward.txt only as a resource label. Say that writing one of the two files is mandatory, that exiting 0 without either scores nothing rather than zero, and that Harbor raises RewardFileNotFoundError. The taught test.sh pattern also produced exactly this failure -- under set -e a failing check aborts before merge_metrics runs -- so it now seeds a reward before anything can fail. Signed-off-by: Cursor Agent <cursoragent@cursor.com> Co-authored-by: Aditya Pandey <aditya@autospace.co>

coderabbitai · 2026-08-17T22:29:28Z

📝 Walkthrough

Walkthrough

Harbor reward documentation now defines required output for success and failure cases. Experimentalist reports explain missing objectives, baseline validation rejects unmeasurable results, and a CLI traces synthetic or real reward pipelines.

Changes

Reward diagnostics and inspection

Layer / File(s)	Summary
Harbor reward output contract `plugins/nemo-experimentalist/src/nemo_experimentalist_plugin/experimentalist/components/evaluator/harbor.py`	Verifier documentation defines required reward files, accepted formats, precedence, missing-reward behavior, and crash handling.
Objective diagnostics and validation `plugins/nemo-experimentalist/src/nemo_experimentalist_plugin/experimentalist/components/models.py`, `plugins/nemo-experimentalist/src/nemo_experimentalist_plugin/experimentalist/context.py`, `plugins/nemo-experimentalist/src/nemo_experimentalist_plugin/experimentalist/reporting.py`, `plugins/nemo-experimentalist/src/nemo_experimentalist_plugin/experimentalist/strategies/evolutionary.py`, `plugins/nemo-experimentalist/tests/experimentalist/*`, `plugins/nemo-experimentalist/tests/test_metric_contract.py`	Missing objective metrics produce trial-based explanations. Reports display supplied reasons. Baseline validation raises `ValueError` for measured results without objective metrics. Tests cover failed, silent, incomplete, and valid evaluations.
Reward pipeline inspector `plugins/nemo-experimentalist/tools/reward_pipeline_inspector.py`	The CLI materializes synthetic Harbor jobs or inspects real jobs, then reports trial files, reward artifacts, aggregation results, operator output, and Pareto metric status.

Sequence Diagram(s)

sequenceDiagram
  participant CLI
  participant HarborJobFiles
  participant TrialsFromJobDir
  participant EvaluatorAggregation
  participant RunReporter
  CLI->>HarborJobFiles: Materialize or select job directory
  CLI->>TrialsFromJobDir: Parse trial results
  TrialsFromJobDir->>EvaluatorAggregation: Provide trial data
  EvaluatorAggregation-->>CLI: Return metrics or ValueError
  CLI->>RunReporter: Render candidate evaluation output
  RunReporter-->>CLI: Return operator-visible report

Possibly related PRs

NVIDIA-NeMo/nemo-platform#955: Shares Harbor reward parsing and evaluator behavior.
NVIDIA-NeMo/nemo-platform#1197: Shares MetricTarget objective handling and metric-contract tests.
NVIDIA-NeMo/nemo-platform#1310: Shares Harbor trial-error and missing-reward diagnostics.

Suggested labels: test

Suggested reviewers: arpitsardhana, sandychapman

🚥 Pre-merge checks | ✅ 4 | ❌ 1

❌ Failed checks (1 warning)

Check name	Status	Explanation	Resolution
Docstring Coverage	⚠️ Warning	Docstring coverage is 68.18% which is insufficient. The required threshold is 80.00%.	Write docstrings for the functions missing them to satisfy the coverage threshold.

✅ Passed checks (4 passed)

Check name	Status	Explanation
Linked Issues check	✅ Passed	Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check	✅ Passed	Check skipped because no linked issues were found for this pull request.
Description Check	✅ Passed	Check skipped - CodeRabbit’s high-level summary is enabled.
Title check	✅ Passed	The title clearly summarizes the primary change: explaining and refusing evaluations that produce no objective metric.

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Inline comments:
In `@plugins/nemo-experimentalist/tools/reward_pipeline_inspector.py`:
- Around line 75-91: Define a concrete TypedDict for trial specifications with
task, reward_text, and exception_type fields, using the existing value types,
then replace the generic dict annotations in Scenario.trials and _trial’s return
type with that TypedDict while preserving the current field values and defaults.
- Around line 238-249: The inspector must report baseline rejection before
describing Pareto ranking outcomes. In the baseline path, use the existing
_record_baseline_validation and missing_objective_reason behavior to print the
rejection condition when an objective is absent; only emit the current
ranking/no-winner explanation for non-baseline evaluations, or gate it behind an
explicit --baseline mode.
- Around line 29-37: Update all reward_pipeline_inspector.py usage examples to
invoke the script directly with uv run, removing the python subcommand while
preserving the existing arguments and scenarios.

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plugins/nemo-experimentalist/src/nemo_experimentalist_plugin/experimentalist/components/evaluator/harbor.py
plugins/nemo-experimentalist/src/nemo_experimentalist_plugin/experimentalist/components/models.py
plugins/nemo-experimentalist/src/nemo_experimentalist_plugin/experimentalist/context.py
plugins/nemo-experimentalist/src/nemo_experimentalist_plugin/experimentalist/reporting.py
plugins/nemo-experimentalist/src/nemo_experimentalist_plugin/experimentalist/strategies/evolutionary.py
plugins/nemo-experimentalist/tests/experimentalist/test_loop_helpers.py
plugins/nemo-experimentalist/tests/experimentalist/test_reporting.py
plugins/nemo-experimentalist/tests/test_metric_contract.py
plugins/nemo-experimentalist/tools/reward_pipeline_inspector.py

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github-actions · 2026-08-17T22:55:23Z

Suite	Lines Covered	Line Rate	Branch Rate
Unit Tests	34296/43312	79.2%	64.0%
Integration Tests	20258/41111	49.3%	22.0%

…ead of advising one The authoring guide told task authors to write `echo 0 > /logs/verifier/reward.txt` before running any check, so a crashing verifier would still leave a reward behind. That defeats the rest of this change. Harbor ignores test.sh's exit code and looks only for a reward file, so a seeded zero makes a verifier that died half-way report a confident 0.0: the trial is 'completed', has_metric_dimensions passes, and the baseline guard never fires. A broken harness becomes indistinguishable from an agent that genuinely scored zero, which is the distinction the contract exists to keep. Say the opposite: score every agent outcome including failure, and let a verifier that cannot finish fail the trial. Drop the seed from the taught test.sh. Signed-off-by: Cursor Agent <cursoragent@cursor.com> Co-authored-by: Aditya Pandey <aditya@autospace.co>

…ts docs Fold the reward-file contract into the existing 'must write to' bullet list in the HarborDataset guide instead of appending three paragraphs after it. The warning against seeding a placeholder went with them: it argued against a pattern the guide no longer shows. Trim missing_objective_reason to the constraint a future editor needs -- why it reads the trials rather than the aggregate, and why it stays free of evaluator vocabulary. Both failure branches now share the completed/total prefix, which reads correctly at any trial count where 'all 1 trials failed' did not. Signed-off-by: Cursor Agent <cursoragent@cursor.com> Co-authored-by: Aditya Pandey <aditya@autospace.co>

- Invoke the script directly with `uv run <path>.py`, per the repo's Python guidance. - Replace the untyped trial-spec dict with a frozen TrialSpec dataclass, which also removes the _trial factory: the constructor already was one. - Stop describing pre-fix behaviour. Layer 4 now passes the reason through the reporter, as ExperimentContext.evaluate does, and layer 5 no longer claims the run reaches ranking and finds no winner -- as a baseline it now stops outright, and the job directory alone cannot say which candidate it belongs to, so both are stated. - Docstring the three helpers that lacked one. Signed-off-by: Cursor Agent <cursoragent@cursor.com> Co-authored-by: Aditya Pandey <aditya@autospace.co>

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Inline comments:
In `@plugins/nemo-experimentalist/tools/reward_pipeline_inspector.py`:
- Around line 249-250: Update the reason-is-None message in the reward pipeline
inspection flow to avoid claiming that a winner exists; state only that the
candidate has all objective dimensions and can participate in ranking, while
leaving the selector and terminator claims unchanged.

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The tool existed to locate which layer dropped a missing verifier reward. That question is answered and the behaviour it demonstrated is now pinned by tests, so keeping it means maintaining a second description of the same pipeline that can drift from the code -- it already had, twice, describing pre-fix narration and a pre-fix ranking outcome. Signed-off-by: Cursor Agent <cursoragent@cursor.com> Co-authored-by: Aditya Pandey <aditya@autospace.co>

github-actions Bot added the chore label Aug 17, 2026

cursoragent and others added 5 commits August 17, 2026 22:03

cursor Bot changed the title ~~chore(experimentalist): add a reward-pipeline inspector for missing verifier rewards~~ fix(experimentalist): explain and refuse an evaluation that produced no objective metric Aug 17, 2026

callingmedic911 marked this pull request as ready for review August 17, 2026 22:26

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github-actions Bot added the fix label Aug 17, 2026

coderabbitai Bot mentioned this pull request Aug 18, 2026

test(experimentalist): add Harbor native vs SDK runtime parity test #1340

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callingmedic911 enabled auto-merge August 18, 2026 20:20

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fix(experimentalist): fix maxed out Author metrics #1382

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fix(experimentalist): explain and refuse an evaluation that produced no objective metric - #1357

fix(experimentalist): explain and refuse an evaluation that produced no objective metric#1357
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Summary

Related Issue

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Why the fix is not in aggregate_results or the terminator

Type of Change

Quality Gates

Verification

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callingmedic911 commented Aug 17, 2026 •

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Why the fix is not in `aggregate_results` or the terminator

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