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The library keeps one immutable, model-agnostic canonical record per imported
attempt. Training examples are projections; they never mutate or truncate the
canonical record. JSON objects are UTF-8 and may be stored as `.jsonl` or
`.jsonl.gz` shards.
## Canonical record
`Trajectory` schema version `1.0` contains:
| Field | Meaning |
|---|---|
| `trajectory_id` | Stable content-addressed ID for one attempt. |
| `task_id` / `task_key` | Native task ID and cross-source normalized task identity. Alternative attempts share `task_key`. |
| `source` | `source_id`, direct URL, immutable revision, row/file locator and optional file SHA-256. |
| `task_family`, `domain`, `split` | Benchmark family, environment domain and the untouched upstream split label. Derived train/evaluation assignment lives in `task_splits.jsonl.gz`. |
| `events` | Causally ordered system, user, assistant and tool/environment events. |
| `tools` | Available function definitions when published. Missing definitions are declared, never invented. |
| `artifacts` | External image/file references and hashes when present. |
| `provenance` | Demonstrator class, producer/scaffold, exact model when supplied and source metadata. |
| `outcome` | Independent `success`, `status`, reward, termination reason and source metadata. `null` success is unknown, never failure. |
| `verification` | Method, whether the source supplied executable verification, and its evidence. Independent reproduction is a separate evidence flag. |
| `completeness` | `complete`, `incomplete` or `truncated`. A final submit call may terminate through an externally recorded outcome without a fabricated tool result. |
| `missing_fields` | Explicit omissions such as unavailable tool definitions or a missing intermediate result. |
| `transformations` | Ordered, versioned adapter operations. |
| `raw_record` | Lossless original row/document. Non-JSON bytes are base64 tagged. |
| `metadata` | Environment/version, trial, timing, cost and other non-causal attributes. |
Each event has a stable `event_id`, role, text content, zero or more structured
tool calls, an optional tool-result link, source metadata, missing fields and a
`supervised` flag. Only assistant events may be supervised. Tool results must
refer to an earlier call. Call IDs reused by an upstream source are namespaced
deterministically while retaining the original ID.
Canonical validation enforces non-empty identifiers, unique event/call IDs,
causal tool-result linkage, assistant-only supervision, declared omissions for
incomplete records and preservation of the raw source. It does not manufacture
observations or reinterpret a missing outcome as failure.
## Independent quality and ranking record
`rankings.jsonl.gz` is a derived index, kept separate so importing another
attempt does not alter canonical records. Every row has separate fields for:
- `demonstration_provenance`: expert/reference demonstration or recorded agent completion;
- `outcome_category`: success, failure or unknown;
- `verification_strength`: independently reproduced, source evaluator, source reported or unverified;
- `completeness`, `source_confidence`, `quality_tier`, numeric tie-break score and `within_task_rank`;
- SFT eligibility, an interaction hash, duplicate provenance and human-readable ranking reasons.
The five tiers are: independently reproduced successful expert; independently
reproduced successful agent; complete demonstration/rollout with credible
source evidence; complete rollout with unknown outcome; failed, partial or
inadequately observed. “Expert” does not mean optimal. No imported trajectory
is called optimal.
Ranks compare only identical `task_key` plus compatible `environment_key`.
Tier dominates; action-observation coverage, verdict reliability, source
confidence and (only for comparable successful attempts) a small efficiency
tie-break follow. Identical interactions receive one interaction hash and list
all contributing trajectory IDs.
The final library collapses records only when causal interaction, outcome,
reward, completeness and verification evidence agree. The retained ranking row
lists every contributing ID; `duplicate_provenance.jsonl.gz` preserves each
collapsed source revision and locator. Action-identical rows with conflicting
outcome evidence remain separate.
## Full-trajectory SFT view
`full_trajectory(record)` emits one sample:
```json
{
"view": "full-trajectory",
"sample_id": "full:...",
"trajectory_id": "trajectory:...",
"task_id": "...",
"task_key": "...",
"events": ["complete causal event sequence"],
"target_event_ids": ["every supervised assistant event ID"],
"model_input": {
"events": ["complete causal event sequence"],
"tool_definitions": ["source-published tools"]
},
"loss_mask": [false, false, true, false, true],
"supervised_targets": ["assistant events selected by the mask"],
"metadata": {"provenance and outcome for filtering/scoring, not model input"}
}
```
System/user text and returned observations provide context. Loss applies only
to assistant events whose mask value is true. A downstream tokenizer maps that
event mask to token positions; this library does not impose a tokenizer, chat
template, truncation or packing policy.
## Assistant-turn SFT view
`assistant_turns(record)` yields one sample per supervised assistant decision:
```json
{
"view": "assistant-turn",
"sample_id": "turn:...",
"trajectory_id": "trajectory:...",
"target_event_id": "event:...",
"target_index": 4,
"context": ["events 0 through 3 only"],
"target": {"the recorded assistant event at index 4"},
"model_input": {
"events": ["events 0 through 3 only"],
"tool_definitions": ["source-published tools"]
},
"supervised_target": {"the recorded assistant event at index 4"},
"metadata": {"parent/provenance/outcome; excluded from model_input"}
}
```
For an assistant event at index `i`, context is exactly `events[:i]`. It cannot
contain the target, its returned observation, later decisions, outcomes,
reference answers or evaluator explanations. Tests assert this invariant.
Consumers may shorten or mask the prefix after projection; canonical events
remain untouched.
## Real example
The successful SWE-Gym attempt
`trajectory:10b50eb527ccd481a86cc00f` targets
`swe:python__mypy-10308` and has 27 events / 12 assistant decisions:
1. OpenHands system instructions.
2. User task and repository context.
3. Assistant tool call.
4. Returned repository listing.
5. Next assistant tool call.
6. Returned observation, continuing until submission and the source test verdict.
Its full view has one record, 27 event-level mask values and 12 targets. Its
turn view has 12 records: turn zero sees events `[:2]`; turn one sees the first
tool result as well; no turn sees a future result.
Concrete outcome cases in the canonical shards are:
- success: `trajectory:10b50eb527ccd481a86cc00f` (SWE-Gym execution result);
- failure: `trajectory:d16d34d7b0acbbd878923aba` (same source, failed tests);
- unknown: `trajectory:bcf4161ed7c363f7e3e03e1c` (TerminalTraj has action/output pairs but no task verdict).
Render any of them with `scripts/inspect_library.py --trajectory-id ...`. The
deterministic gallery independently selects compact representatives for every
available source/outcome/tier combination and exposes each full interaction,
every causal turn sample, the exact model-input object, supervised target,
verification evidence, missing fields and side-by-side alternatives.
## Consumer selections
```python
dataset.alternatives("swe:python__mypy-10308") # task-ranked alternatives
dataset.best_verified("swe:python__mypy-10308")
dataset.by_quality_tier(1, 2, 3)
```
For the large persisted library, use `scripts/export_sft_views.py` with
`--selection best`, `successful` or `all`, plus optional benchmark, source,
source-version, model, scaffold, split, quality-tier and quality-rank filters.
Deterministic random, task-level and source-balanced sampling use the same
`CanonicalDatasetAdapter` contract documented in
[dataset_adapter.md](dataset_adapter.md). Expert-only means filtering
`demonstration_provenance` in the ranking index; successful-completion-only
means `outcome_category=success` plus the recorded-agent provenance.
`task_splits.jsonl.gz` assigns one split per `task_key` across every source and
both derived views.
## Persistent library layout
The default library is manifest-backed: `manifest.json` lists every lossless
pinned canonical shard with its path, SHA-256 and record count. Passing
`--materialize-all` can additionally create `all.jsonl.gz`. To avoid duplicating
very large raw records, outcome and eligibility partitions are compact
trajectory-ID indexes:
- `successful.index.jsonl.gz`, `unsuccessful.index.jsonl.gz`, and
`unknown.index.jsonl.gz`;
- `structurally_eligible.index.jsonl.gz` for records passing the causal
action-observation gate;
- `training_eligible.index.jsonl.gz` for structurally suitable records whose
source is not protected/evaluation-only.
`index.sqlite` joins those concepts without loading canonical JSONL and adds
source, model, scaffold, task, rank, outcome and quality fields.
`rankings.jsonl.gz` and `task_splits.jsonl.gz` are portable derived indexes.
`duplicate_provenance.jsonl.gz` records exact rows collapsed into each retained
canonical interaction. Partition membership never changes or copies canonical
content.
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