0ea6c34325
Introduced a new evaluation environment for OpenThoughts-TBLite, including the main evaluation script, configuration YAML, and README documentation. This environment provides a faster alternative to Terminal-Bench 2.0, featuring 100 difficulty-calibrated tasks for terminal agents. The setup allows for easy evaluation and configuration, enhancing the benchmarking capabilities for terminal agents.
74 lines
3.6 KiB
Markdown
74 lines
3.6 KiB
Markdown
# OpenThoughts-TBLite Evaluation Environment
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This environment evaluates terminal agents on the [OpenThoughts-TBLite](https://huggingface.co/datasets/open-thoughts/OpenThoughts-TBLite) benchmark, a difficulty-calibrated subset of [Terminal-Bench 2.0](https://www.tbench.ai/leaderboard/terminal-bench/2.0).
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## Source
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OpenThoughts-TBLite was created by the [OpenThoughts](https://www.openthoughts.ai/) Agent team in collaboration with [Snorkel AI](https://snorkel.ai/) and [Bespoke Labs](https://bespokelabs.ai/). The original dataset and documentation live at:
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- **Dataset (source):** [open-thoughts/OpenThoughts-TBLite](https://huggingface.co/datasets/open-thoughts/OpenThoughts-TBLite)
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- **GitHub:** [open-thoughts/OpenThoughts-TBLite](https://github.com/open-thoughts/OpenThoughts-TBLite)
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- **Blog post:** [openthoughts.ai/blog/openthoughts-tblite](https://www.openthoughts.ai/blog/openthoughts-tblite)
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## Our Dataset
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We converted the source into the same schema used by our Terminal-Bench 2.0 environment (pre-built Docker Hub images, base64-encoded test tarballs, etc.) and published it as:
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- **Dataset (ours):** [NousResearch/openthoughts-tblite](https://huggingface.co/datasets/NousResearch/openthoughts-tblite)
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- **Docker images:** `nousresearch/tblite-<task-name>:latest` on Docker Hub (100 images)
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The conversion script is at `scripts/prepare_tblite_dataset.py`.
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## Why TBLite?
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Terminal-Bench 2.0 is one of the strongest frontier evaluations for terminal agents, but when a model scores near the floor (e.g., Qwen 3 8B at <1%), many changes look identical in aggregate score. TBLite addresses this by calibrating task difficulty using Claude Haiku 4.5 as a reference:
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| Difficulty | Pass Rate Range | Tasks |
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|------------|----------------|-------|
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| Easy | >= 70% | 40 |
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| Medium | 40-69% | 26 |
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| Hard | 10-39% | 26 |
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| Extreme | < 10% | 8 |
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This gives enough solvable tasks to detect small improvements quickly, while preserving enough hard tasks to avoid saturation. The correlation between TBLite and TB2 scores is **r = 0.911**.
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TBLite also runs 2.6-8x faster than the full TB2, making it practical for iteration loops.
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## Usage
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```bash
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# Run the full benchmark
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python environments/benchmarks/tblite/tblite_env.py evaluate
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# Filter to specific tasks
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python environments/benchmarks/tblite/tblite_env.py evaluate \
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--env.task_filter "broken-python,pandas-etl"
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# Use a different model
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python environments/benchmarks/tblite/tblite_env.py evaluate \
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--server.model_name "qwen/qwen3-30b"
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```
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## Architecture
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`TBLiteEvalEnv` is a thin subclass of `TerminalBench2EvalEnv`. All evaluation logic (agent loop, Docker sandbox management, test verification, metrics) is inherited. Only the defaults differ:
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| Setting | TB2 | TBLite |
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|----------------|----------------------------------|-----------------------------------------|
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| Dataset | `NousResearch/terminal-bench-2` | `NousResearch/openthoughts-tblite` |
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| Tasks | 89 | 100 |
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| Task timeout | 1800s (30 min) | 1200s (20 min) |
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| Wandb name | `terminal-bench-2` | `openthoughts-tblite` |
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## Citation
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```bibtex
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@software{OpenThoughts-TBLite,
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author = {OpenThoughts-Agent team, Snorkel AI, Bespoke Labs},
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month = Feb,
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title = {{OpenThoughts-TBLite: A High-Signal Benchmark for Iterating on Terminal Agents}},
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howpublished = {https://www.openthoughts.ai/blog/openthoughts-tblite},
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year = {2026}
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}
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```
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