Datasets:

Heisenger
/

sft-benchmark-trajectories

---
license: other
pretty_name: SFT Benchmark Trajectories
task_categories:
- text-generation
tags:
- agent-trajectories
- sft
- tool-use
- software-engineering
size_categories:
- 100K<n<1M
---
# SFT Benchmark Trajectories

A model-agnostic collection of real expert demonstrations and recorded agent
completions, normalized into one interface for full-trajectory and causal
assistant-turn SFT.

## At a glance

- **260,753 canonical records** across **154,244 task keys**
- **13 benchmark families** and **21 trajectory sources**
- **235,089 hosted payloads** plus **25,664 metadata-only records**
- **67,017 success-labelled**, **88,350 failure-labelled**, and **105,386 unknown-outcome records**
- Task-disjoint train, validation, test and protected-evaluation splits
- Implementation version: `v0.5.0` (installable wheel included)

The inventory includes full recorded trajectories and explicitly marked
assistant-turn prefixes. Prefix records provide exactly one supervised answer
with its published context; they are never presented as complete episodes.

## Priority SFT selections

| Source | Available selection | Training views | Outcome caveat |
|---|---|---|---|
| AReaL τ² SFT | `areal_tau2_author_sft`: all 33,531 original targets | 1,824 reconstructed full dialogs; all retained records support turns | `correct` and `reward` disagree on 2,078 original rows; both are preserved |
| LiteCoder Terminal SFT | `litecoder_terminal_author_sft`: 11,255 original conversations | Full recorded interaction and assistant turns | No per-record grader score; self-claimed completion is not verified success |
| ToolBench / ToolLLaMA | `toolbench_valid_solution_paths`: 73,121 existing paths | Full and turn | Official valid-data selection; semantic success unavailable |
| SWE-smith | `swesmith_sweagent_lm32b_author`: 3,567 existing successful records | Full and turn | Exact intersection with 4,954 author-published IDs; 1,387 are unavailable in our tool serialization |
| SWE-Gym OpenHands SFT | `swegym_openhands_author_sft`: all 491 existing successful records | Full and turn | Published success-only split; no local replay |

These are selections over the same canonical records. Selecting them adds no
duplicate trajectory copies. The [SFT expansion audit](docs/v0.5.0_sft_expansion.md)
documents the exact source releases, preserved target counts and limitations.

Historical outcomes below are the results attached by each source. This project
did not rerun the benchmark environments. A source score describes the imported
attempts, not the expected score of a newly trained model.

## Benchmarks

| Benchmark | What it tests | Native or published metric | Tasks | Records |
|---|---|---|---:|---:|
| [BrowseComp](https://github.com/openai/simple-evals) | Research-style web browsing and answer synthesis. | answer correctness (llm judge) | 1,266 | 5,063 |
| [Mind2Web](https://osu-nlp-group.github.io/Mind2Web/) | Human demonstrations for completing tasks on real websites. | step and element accuracy (action match) | 1,009 | 1,009 |
| [SWE-bench](https://www.swebench.com/) | Resolve real software repository issues. | resolved (native binary) | 34,244 | 56,898 |
| [SWE-Gym](https://github.com/SWE-Gym/SWE-Gym) | Train and evaluate software-engineering agents on repository tasks. | execution-test success (native binary) | 2,731 | 5,993 |
| [SWE-smith](https://github.com/SWE-bench/SWE-smith) | Generated software-engineering tasks with executable validation. | resolved (native binary) | 10,637 | 24,100 |
| [τ2-bench](https://github.com/sierra-research/tau2-bench) | Tool-using agents interacting with simulated users in service domains. | evaluator correctness and reward (native reward) | 2,727 | 21,962 |
| [τ3-bench](https://github.com/sierra-research/tau2-bench) | Tool-using agents and simulated users with environment reward evidence. | environment/action/communication reward (native reward) | 50 | 200 |
| [Terminal-Bench](https://github.com/laude-institute/terminal-bench) | Complete practical tasks in sandboxed terminal environments. | task reward (native reward) | 10,161 | 45,518 |
| [TerminalTraj](https://huggingface.co/datasets/m-a-p/TerminalTraj) | A corpus of terminal command and observation trajectories. | not available (none) | 17,787 | 20,000 |
| [ToolBench](https://github.com/OpenBMB/ToolBench) | Tool-use planning and API-call trajectories. | valid-data/win generation status (source proxy) | 73,132 | 73,132 |
| [BFCL V4](https://gorilla.cs.berkeley.edu/leaderboard.html) | Agentic function-calling tasks with web and memory tool environments. | BFCL evaluator score (native binary) | 665 | 6,160 |
| [DeepSWE](https://huggingface.co/datasets/yuzhenm/delm-720-trajectories) | Repository engineering tasks executed by multi-agent DeLM teams. | CTRF test-suite pass (native binary) | 10 | 360 |
| [Terminal-Bench 4](https://huggingface.co/datasets/yuzhenm/delm-720-trajectories) | Protected terminal tasks executed by multi-agent DeLM teams. | task reward (native reward) | 10 | 358 |

Benchmark rows are not additive: **185 task keys** appear in
more than one benchmark family.

Metrics are deliberately not combined into one global score. Binary repository
tests, scalar rewards, LLM judges and unscored human demonstrations are not
interchangeable.

## Trajectory sources and observed performance

| Benchmark | Trace source and origin | Source ID | Tasks | Records | Score in imported slice | Score context | Payload |
|---|---|---|---:|---:|---|---|---|
| BrowseComp | [BrowseComp GPT-OSS-120B Rollouts](https://huggingface.co/datasets/rl-rag/browsecomp-gpt-oss-120b-260222) | `browsecomp_rollouts` | 1,266 | 5,063 | 23.92% success | 5,063 scored attempts; Source-reported GPT-4o answer judge; not independently reproduced | metadata_only |
| Mind2Web | [Mind2Web](https://huggingface.co/datasets/osunlp/Mind2Web) | `mind2web` | 1,009 | 1,009 | not available | task outcome unavailable; Per-action DOM context and human annotation are present; no explicit binary execution-success field | hosted |
| SWE-bench | [NVIDIA Open-SWE-Traces](https://huggingface.co/datasets/nvidia/Open-SWE-Traces) | `nvidia_open_swe_traces` | 33,444 | 51,626 | 47.93% success | 51,626 scored attempts; Published resolved field; not independently replayed here | hosted |
| SWE-bench | [SWE-bench Multilingual GLM-5 Trajectories](https://huggingface.co/datasets/AlienKevin/SWE-bench-multilingual-glm-5-trajectories) | `swebench_multilingual_glm5` | 300 | 300 | 69.67% success | 300 scored attempts; Published resolved field | metadata_only |
| SWE-bench | [SWE-bench Multilingual MiniMax-M2.5 Trajectories](https://huggingface.co/datasets/AlienKevin/SWE-bench-multilingual-minimax-m2.5-trajectories) | `swebench_multilingual_minimax_m25` | 299 | 299 | 73.58% success | 299 scored attempts; Published resolved field | metadata_only |
| SWE-bench | [SWE-bench Multilingual Trajectories](https://huggingface.co/datasets/AlienKevin/SWE-bench-Multilingual-trajectories) | `swebench_multilingual_trajectories` | 43 | 129 | 6.20% success | 129 scored attempts; Published resolved/completed/errored fields | hosted |
| SWE-bench | [SWE-bench Verified mini-swe-agent Trajectories](https://huggingface.co/datasets/tarsur385/swebench-verified-trajectories) | `swebench_verified_trajectories` | 500 | 4,544 | 74.65% success | 4,544 scored attempts; Published SWE-bench execution result; protected evaluation split | hosted |
| SWE-Gym | [SWE-Gym OpenHands SFT Trajectories](https://huggingface.co/datasets/SWE-Gym/OpenHands-SFT-Trajectories) | `swegym_openhands` | 293 | 491 | 100.00% success | success-only release; Published train.success.oss split label; not independently replayed | hosted |
| SWE-Gym | [SWE-Gym OpenHands Sampled Trajectories](https://huggingface.co/datasets/SWE-Gym/OpenHands-Sampled-Trajectories) | `swegym_sampled` | 2,438 | 5,502 | 8.92% success | 5,502 scored attempts; Source execution test result; not independently replayed here | metadata_only |
| SWE-smith | [SWE-smith Trajectories](https://huggingface.co/datasets/SWE-bench/SWE-smith-trajectories) | `swesmith_trajectories` | 10,637 | 24,100 | 39.12% success | 24,100 scored attempts; Published resolved field | hosted |
| τ2-bench | [AgentSuite tau2-bench trajectories](https://huggingface.co/datasets/AgentSuite/tau2-bench-trajectories) | `agentsuite_tau2` | 228 | 8,340 | 0.478 mean reward; 40.47% success | 8,340 scored attempts; AgentSuite evaluator fields; meta.is_correct is used for the binary outcome | metadata_only |
| τ2-bench | [AReaL tau2 SFT](https://huggingface.co/datasets/inclusionAI/AReaL-tau2-data) | `areal_tau2_sft` | 2,499 | 13,622 | 0.798 mean reward; 74.46% correct labels | 33,531 targets; published dialog labels, weighted over original supervised targets; Source correctness/reward labels, with 2,078 turn rows whose labels disagree; no grader or independent replay | hosted |
| τ3-bench | [Official tau3-bench](https://github.com/sierra-research/tau2-bench) | `official_tau3` | 50 | 200 | 0.500 mean reward; 50.00% success | 200 scored attempts; Environment/action/communication reward evidence | hosted |
| Terminal-Bench | [Terminal-Bench 2.0 Recorded Trajectories](https://huggingface.co/datasets/yoonholee/terminalbench-trajectories) | `terminalbench_trajectories` | 89 | 34,263 | 0.323 mean reward; 32.25% success | 34,263 scored attempts; Source-reported Terminal-Bench reward | hosted |
| Terminal-Bench | [LiteCoder Terminal SFT](https://huggingface.co/datasets/Lite-Coder/LiteCoder-Terminal-SFT) | `litecoder_terminal_sft` | 10,072 | 11,255 | not available | task outcome unavailable; Original SFT selection; agent task_complete claims are not grader evidence | hosted |
| TerminalTraj | [TerminalTraj](https://huggingface.co/datasets/m-a-p/TerminalTraj) | `terminaltraj` | 17,787 | 20,000 | not available | task outcome unavailable; Paired command/output transcript; no trustworthy task-level success field | hosted |
| ToolBench | [ToolBench](https://github.com/OpenBMB/ToolBench) | `toolbench` | 11 | 11 | 63.64% success | 11 scored attempts; valid_data/win generation status; no independent semantic proof | hosted |
| ToolBench | [ToolBench Full Valid Solution Paths](https://github.com/OpenBMB/ToolBench) | `toolbench_full` | 73,121 | 73,121 | not available | task outcome unavailable; Official preprocessing includes only valid_data paths; search-win/task-success labels are absent | hosted |
| BFCL V4 | [AgentSuite BFCL V4 Agentic Trajectories](https://huggingface.co/datasets/AgentSuite/BFCL_V4-trajectories) | `agentsuite_bfcl_v4` | 665 | 6,160 | 0.784 mean reward; 78.43% success | 6,160 scored attempts; Published BFCL V4 evaluator score | metadata_only |
| DeepSWE | [DeLM DeepSWE Multi-Agent Trajectories](https://huggingface.co/datasets/yuzhenm/delm-720-trajectories) | `delm_deepswe` | 10 | 360 | 0.917 mean reward; 91.67% success | 360 scored attempts; Published DeepSWE CTRF test report | hosted |
| Terminal-Bench 4 | [DeLM Terminal-Bench 4 Multi-Agent Trajectories](https://huggingface.co/datasets/yuzhenm/delm-720-trajectories) | `delm_terminalbench4` | 10 | 358 | 0.863 mean reward; 86.27% success | 357 scored attempts; Published Terminal-Bench 4 grader reward | hosted |

`success-only release` means the dataset was conditioned on a successful result;
it does not imply that the generating agent had a 100% policy success rate.
`metadata_only` sources retain scores, lineage, hashes and pinned source pointers
but require a consumer-authorized content overlay.
Aligned synthetic SFT sources contain their own training tasks. Their task
counts do not represent the size of the official benchmark evaluation set.

## Model context

The following rows contextualize trace strength where one source contains
multiple sufficiently covered models. Models are scored on the intersection of
their task keys within that source. Representative lower, median and higher
rows are shown; they are not cross-benchmark rankings.

| Benchmark | Source | Model | Position | Common tasks | Scored attempts | Success |
|---|---|---|---|---:|---:|---:|
| BFCL V4 | `agentsuite_bfcl_v4` | `openai/gpt-4.1-mini` | lower | 91 | 91 | 82.42% |
| BFCL V4 | `agentsuite_bfcl_v4` | `deepseek-ai/DeepSeek-V3.2-Exp-thinking-off` | median | 91 | 91 | 87.91% |
| BFCL V4 | `agentsuite_bfcl_v4` | `deepseek-ai/DeepSeek-V3.2-Exp-thinking-on` | higher | 91 | 91 | 91.21% |
| τ2-bench | `agentsuite_tau2` | `togetherai/openai/gpt-oss-20b` | lower | 228 | 278 | 10.79% |
| τ2-bench | `agentsuite_tau2` | `deepseek-ai/DeepSeek-R1-0528` | median | 228 | 278 | 43.53% |
| τ2-bench | `agentsuite_tau2` | `anthropic/claude-4.5-sonnet-thinking-on-10k` | higher | 228 | 278 | 54.68% |
| DeepSWE | `delm_deepswe` | `claude-opus-5-5` | lower | 10 | 180 | 90.56% |
| DeepSWE | `delm_deepswe` | `gpt-6-astra` | higher | 10 | 180 | 92.78% |
| Terminal-Bench 4 | `delm_terminalbench4` | `gpt-6-astra` | lower | 10 | 180 | 80.56% |
| Terminal-Bench 4 | `delm_terminalbench4` | `claude-opus-5-5` | higher | 10 | 177 | 92.09% |
| SWE-bench | `nvidia_open_swe_traces` | `Qwen3.8-27B` | lower | 18,182 | 18,182 | 50.71% |
| SWE-bench | `nvidia_open_swe_traces` | `Qwen3.8-Flash-Next` | higher | 18,182 | 18,182 | 53.60% |
| SWE-bench | `swebench_multilingual_trajectories` | `Qwen2.5-Coder-32B-Instruct` | lower | 43 | 43 | 0.00% |
| SWE-bench | `swebench_multilingual_trajectories` | `Multi-SWE-agent-32B-Rust` | median | 43 | 43 | 2.33% |
| SWE-bench | `swebench_multilingual_trajectories` | `SWE-agent-LM-32B` | higher | 43 | 43 | 16.28% |
| SWE-bench | `swebench_verified_trajectories` | `gpt-5-mini` | lower | 44 | 44 | 40.91% |
| SWE-bench | `swebench_verified_trajectories` | `claude-4.5-opus-high` | median | 44 | 44 | 70.45% |
| SWE-bench | `swebench_verified_trajectories` | `gemini-3-pro` | higher | 44 | 44 | 100.00% |
| Terminal-Bench | `terminalbench_trajectories` | `openai/gpt-oss-20b@together_ai` | lower | 80 | 1,627 | 3.56% |
| Terminal-Bench | `terminalbench_trajectories` | `gpt-5.1-codex@openai` | median | 80 | 396 | 39.39% |
| Terminal-Bench | `terminalbench_trajectories` | `claude-opus-4.6@anthropic` | higher | 80 | 153 | 76.47% |

Sources without at least two models covering half of the same source task set
are omitted rather than compared on unrelated questions. Full model, scaffold
and per-trajectory results remain queryable in `index.sqlite` and
`evaluation/summary.json`.

No comparable multi-model slice is available here for: BrowseComp, Mind2Web, SWE-Gym, SWE-smith, τ3-bench, TerminalTraj, ToolBench.
Those benchmarks have one recorded model, human/unknown provenance, no outcome,
or model runs on materially different task slices.

## Install and download

```bash
python3 -m venv .venv
.venv/bin/pip install 'https://huggingface.co/datasets/Heisenger/sft-benchmark-trajectories/resolve/v0.5.0/code/agent_trajectory_datasets-0.5.0-py3-none-any.whl'
.venv/bin/pip install huggingface_hub
.venv/bin/hf download Heisenger/sft-benchmark-trajectories --repo-type dataset --revision v0.5.0 --local-dir ./sft-benchmark-trajectories
```

Normal training reads this persisted release. It does not re-download the
upstream benchmark datasets.
The release includes an installable Python wheel. GitHub publication of the
v0.5.0 implementation is pending restored repository authentication; the wheel
provides the complete consumer API in the meantime.

## Two SFT views from the same record

The consumer selects a view at iteration time. Both views preserve the same
task, trajectory, source, split, outcome, reward, verification, quality and
permission lineage.

```python
from agent_trajectories import CanonicalDatasetAdapter, DatasetQuery, TrainingView
dataset = CanonicalDatasetAdapter("./sft-benchmark-trajectories")
query = DatasetQuery(trajectory_ids=("trajectory:f9cf8588a314fa9804d57402",))
full = next(dataset.iter_examples(query, view=TrainingView.FULL_TRAJECTORY))
turns = list(dataset.iter_examples(query, view=TrainingView.ASSISTANT_TURN))
```

### Full-trajectory view

One training sample contains the complete causal trace. The event loss mask
selects every supervised assistant event.

```json
{
  "view": "full-trajectory",
  "sample_id": "full:736e63752d3cff92f0d883d1",
  "trajectory_id": "trajectory:f9cf8588a314fa9804d57402",
  "task_key": "tau:airline:0",
  "event_count": 13,
  "assistant_targets": 6,
  "loss_mask": [false, true, false, true, false, true, false, true, false, true, false, true, false],
  "metadata": {"source_id": "official_tau3", "success": true}
}
```

### Assistant-turn view

The same trajectory produces one sample per supervised assistant decision.
Each sample contains only its causal prefix and one target; prior assistant
tokens are context and future observations are absent.

```json
{
  "view": "assistant-turn",
  "sample_id": "turn:61eec58539653d88c1971b68",
  "trajectory_id": "trajectory:f9cf8588a314fa9804d57402",
  "task_key": "tau:airline:0",
  "target_index": 5,
  "context_events": 5,
  "target": {
    "role": "assistant",
    "content": "Let me check the details of your reservation...",
    "tool_call": {
      "name": "get_reservation_details",
      "arguments": {"reservation_id": "EHGLP3"}
    }
  },
  "metadata": {"source_id": "official_tau3", "success": true}
}
```

Full-trajectory SFT teaches the complete multi-step behavior in one sequence.
Turn-based SFT teaches individual decisions conditioned on the state available
at that turn. Model-specific tokenization maps the event targets to token-level
labels downstream.

## Select a training population

```python
from agent_trajectories import (
    CanonicalDatasetAdapter,
    DatasetQuery,
    TrainingView,
    TrajectorySelection,
)
dataset = CanonicalDatasetAdapter("./sft-benchmark-trajectories")
query = DatasetQuery.for_training(
    splits=("train",),
    content_availability=("hosted",),
    training_permissions=("permitted", "permitted_with_conditions"),
    training_verifications=("verified",),
    selection=TrajectorySelection.BEST_VERIFIED,
)
for example in dataset.iter_examples(
    query,
    view=TrainingView.FULL_TRAJECTORY,
):
    row = example.to_dict()
    model_input = row["model_input"]
    event_loss_mask = row["loss_mask"]
    lineage = row["metadata"]["lineage"]
```

Use `ALL_SUCCESSFUL` instead of `BEST_VERIFIED` to retain multiple successful
strategies per task. Use all attempts for evaluation or preference construction,
not as an undifferentiated positive-SFT target.

Author selections and view availability are additional independent filters:

```python
query = DatasetQuery.for_training(
    source_ids=("areal_tau2_sft",),
    curated_selections=("areal_tau2_author_sft",),
    supported_views=("assistant_turn",),
    splits=("train",),
    selection=TrajectorySelection.ALL_SUCCESSFUL,
)
for example in dataset.iter_examples(query, view=TrainingView.ASSISTANT_TURN):
    row = example.to_dict()  # one target; earlier assistant events are context
```

For LiteCoder, select its author SFT collection with `ALL_ATTEMPTS`: its grader
outcomes are unknown. `available_curated_selections()` lists pinned selection
metadata. `for_training` always excludes protected-evaluation task assignments.

Benchmark, source and demonstration provenance are independently selectable.
For example, this selects successful recorded completions from two exact
sources while keeping protected evaluation traces out of a training export:

```python
query = DatasetQuery.for_training(
    benchmarks=("swe-bench", "swe-smith"),
    source_ids=("nvidia_open_swe_traces", "swesmith_trajectories"),
    demonstration_provenance=("recorded_agent_completion",),
    splits=("train",),
    selection=TrajectorySelection.ALL_SUCCESSFUL,
)
```

Use `demonstration_provenance=("expert_or_reference_demonstration",)` for
expert/reference-only selection. `available_sources()` lists exact source IDs;
`metadata(query)` can inspect a selection without loading trajectory payloads.

## Why the metadata is exposed

| Metadata | Training or evaluation purpose |
|---|---|
| Task and environment keys | Enforce leakage-safe splits and compare compatible attempts |
| Source, model and scaffold | Filter, balance and audit heterogeneous generators |
| Outcome, reward and verification | Select positive traces and define the source-performance reference |
| Quality tier and task-local rank | Choose the best available alternative without treating the score as globally comparable |
| Event/action counts | Detect short, long, incomplete or looping traces |
| Token length | Plan truncation, packing, batch size and compute for a named tokenizer |
| Permissions and payload status | Build a project-specific compliant subset |
| Revision and record locator | Reproduce the exact upstream record |

Token length is tokenizer-specific, not a canonical measure of quality. The
published Nemotron example always names the tokenizer and immutable revision.

## Documentation

- [Consumer API and sampling](docs/dataset_adapter.md)
- [Canonical schema and ranking](docs/trajectory_schema.md)
- [Scoring and evaluation protocol](docs/evaluation_protocol.md)
- [Source provenance and ingestion](docs/trajectory_sources.md)
- [v0.4.0 incremental audit](docs/v0.4.0_incremental_audit.md)
- [v0.5.0 SFT expansion](docs/v0.5.0_sft_expansion.md)
- [Licensing, permissions and federated overlays](docs/licensing_and_federation.md)
- [Nemotron tokenization and assistant-only masks](docs/nemotron_sft_demo.md)
- [Third-party notices](THIRD_PARTY_NOTICES.md)

## Limitations

- Source outcomes were not independently replayed.
- Some sources publish no trustworthy task-level outcome.
- Scores from different benchmark metrics or task slices are not directly
  comparable.
- Some actions and observations are text-encoded rather than typed tool calls.
- `training_eligible` is a structural/source gate; permission filters remain a
  separate consumer decision.
- Metadata-only sources require direct retrieval under the consumer's own
  authorization.
- The audited ALFWorld replayed-expert source remains gated and is not counted
  in this release because the publishing account has not accepted its access
  terms; its pinned locator and permission state remain in the registry.