| # Evaluation protocol |
|
|
| The library evaluates imported evidence; it does not reinterpret a historical |
| rollout as a score for a new target model. Every canonical attempt is retained |
| and receives one row in `data/library/evaluation/per_trajectory.jsonl.gz`. |
| Aggregates are regenerated from that table and `data/library/index.sqlite`. |
|
|
| ## A. Dataset and trajectory quality |
|
|
| `scripts/build_complete_library.py` validates every canonical record, then |
| records the following independent fields: |
|
|
| - structural validity: canonical IDs, roles, ordering and tool-call linkage; |
| - action-observation completeness: the fraction of supervised decisions with |
| source-derived context since the preceding decision; |
| - outcome availability: success/failure is known versus genuinely unknown; |
| - verification reliability: independently reproduced, source evaluator, |
| source-reported or unverified; |
| - structural SFT eligibility: valid complete record, at least two assistant |
| decisions and full action-context coverage; |
| - training eligibility: structural eligibility plus the source registry gate. |
|
|
| Protected benchmark rollouts can be structurally suitable while remaining |
| ineligible for training. Final-answer-only BFCL outputs are excluded from the |
| trajectory population entirely; their audited rejection count remains in the |
| coverage manifest. Unknown outcomes are never converted to failures. |
|
|
| Quality tiers and within-task ranks use the policy in |
| [`trajectory_schema.md`](trajectory_schema.md). Ranks are recomputed within |
| the same `task_key` and compatible environment/version. Exact duplicate |
| interactions retain all contributing trajectory IDs. |
|
|
| ## B. Recorded-agent performance |
|
|
| The baseline uses the outcome and verification evidence published with each |
| rollout. A success is independently reproduced only when the canonical |
| evidence explicitly says so. In this build no benchmark container was replayed, |
| so every measured outcome is labelled `source_evaluator` or |
| `source_reported`; `independently_reproduced` is zero. |
|
|
| The persisted summary reports: |
|
|
| - every attempt and unique task, including unsuccessful alternatives; |
| - success, failure and unknown denominators separately; |
| - outcomes by source, model, scaffold, task family and quality tier; |
| - task/environment attempt-count distributions; |
| - fraction of tasks solved at least once; |
| - empirical success-at-k for groups with at least k recorded attempts, ordered |
| by the immutable source locator. This is descriptive and is not estimated |
| pass@k; |
| - action counts for all records and cost/latency/token fields where upstream |
| rows publish them. |
|
|
| Cost and latency are not aggregated across incompatible benchmarks or units. |
| Raw values remain queryable in SQLite and the per-trajectory result file. |
|
|
| ## C. Model imitation |
|
|
| The two causal SFT views and exact structured next-action scorer are |
| implemented. `trajdata evaluate-actions` compares per-turn assistant text and |
| tool calls without using later observations. A scalable held-out export is: |
|
|
| ```bash |
| .venv/bin/python scripts/export_sft_views.py data/library \ |
| --view assistant-turn --selection all --split validation \ |
| --output outputs/validation-turns.jsonl.gz |
| trajdata evaluate-actions heldout-canonical.jsonl predictions.jsonl |
| ``` |
|
|
| Teacher-forced token negative log-likelihood requires a specified model, |
| tokenizer/chat template and inference runtime. None is configured in this |
| repository or environment, so it was not fabricated. The machine-readable |
| status and reason are persisted in |
| `data/library/evaluation/model_imitation.json`. |
|
|
| ## Leakage controls |
|
|
| Task split assignment hashes `task_key`, not trajectory ID. All attempts, |
| sources and derived full/turn views for the same task therefore share a split. |
| Sources registered as evaluation-only are assigned |
| `protected_evaluation`; `scripts/export_sft_views.py` additionally requires |
| `training_eligible=1`, so protected rows cannot enter its SFT output. Outcome, |
| reward, reference answers, later decisions and future observations are metadata |
| and never appear in a turn's `model_input`. |
|
|
| ## Reproduction |
|
|
| After completing the pinned imports listed in |
| [`trajectory_sources.md`](trajectory_sources.md), rebuild all scores with: |
|
|
| ```bash |
| .venv/bin/python scripts/build_complete_library.py \ |
| data/full/canonical/seed_evaluation/official_tau3_toolbench.jsonl.gz \ |
| --input-dir data/full/canonical/swegym_openhands_full \ |
| --input-dir data/full/canonical/swegym_sampled_v4_pinned \ |
| --input-dir data/full/canonical/swebench_verified_v2 \ |
| --input-dir data/full/canonical/swebench_multilingual \ |
| --input-dir data/full/canonical/terminalbench_v3 \ |
| --input-dir data/full/canonical/agentsuite_tau2_v3 \ |
| --input-dir data/full/canonical/browsecomp \ |
| --input-dir data/full/canonical/nvidia_v1_2_openhands_original \ |
| --input-dir data/full/canonical/terminaltraj_full \ |
| --input-dir data/full/canonical/mind2web_full \ |
| --input-dir data/full/canonical/swesmith_tool_full \ |
| --output data/library |
| |
| .venv/bin/python scripts/build_coverage_manifest.py data/library \ |
| --output data/coverage/trajectory_sources.json |
| .venv/bin/python scripts/render_complete_gallery.py data/library \ |
| --output data/library/examples.html |
| ``` |
|
|
| AgentSuite BFCL is deliberately absent from the build command because its |
| 115,466 records have no returned observations and are final-call evaluation |
| outputs rather than trajectories. |
|
|
