license: other
viewer: false
license_name: fasterlabeling-evaluation-license
license_link: LICENSE
pretty_name: 'FasterLabeling Showcase: three terminal tasks that frontier models fail'
language:
- en
tags:
- terminal-bench
- harbor
- rl-environments
- agentic-coding
- code
- evaluation
size_categories:
- n<1K
FasterLabeling showcase: three terminal tasks that frontier models fail
Three Terminal-Bench style tasks in the Harbor task format, each with a Docker environment, a deterministic grader (a program, not a model judge), a reference solution that scores 1, and the full record of real frontier-model runs that scored 0.
FasterLabeling builds hard coding and terminal RL environments for frontier labs. This set is the public sample. Everything in it is ours: seeded hidden items, no personal data, no client work. The only third-party code is the unmodified open-source release each task reads (pulled at image build time and pinned by hash).
The tasks
| task | what the agent must do | hidden items | expert estimate |
|---|---|---|---|
never-learned-replies |
A nightly fine-tuning job trains on an endless canary chat. From the sources of TRL, transformers and tokenizers alone, decide which replies some history can never learn. | 310 plans, exact answers | ~3 h |
attention-mask-fill-signoff |
For 40 CPU transformer deployments, give the exact interval of additive-mask fill values that are safe, from what the pinned transformers and PyTorch CPU kernels compute in float16 and bfloat16. | 40 deployments, exact intervals | ~4 h |
ram-boot-image |
A generator codes a RAM or ROM in many Verilog styles. From the Yosys 0.70 source alone (no synthesis tool, no compiler), predict what four FPGA flows make of it: cell type, cell count, flip-flop bits, and the exact INIT contents of every cell. | 256 queries, exact answers | ~3 h |
Each task is graded all-or-nothing by tests/test.sh, which writes /logs/verifier/reward.txt. The grader and its
answer key never enter the agent's image. The grader runtime is pinned by SHA-256 (tests/data/runtime_pins.txt)
and checked before grading.
Results
Every run: the task's own container with the agent added, the instruction file as the only input, no network except the model's API, 2 hours of agent time, 4 CPUs, 8 GB. Each deliverable was graded twice, in the agent's container and in a fresh container that received only the deliverable, with identical results. "Wrong" counts the items the grader rejected; one wrong item is a score of 0.
| task | model | runs | outcome |
|---|---|---|---|
| never-learned-replies | claude-fable-5.1 | 2 | fail, 57 / 310 and 125 / 310 wrong |
| never-learned-replies | claude-opus-5.5 | 3 | fail, 4 / 310, 20 / 310, 12 / 310 wrong |
| never-learned-replies | claude-opus-5.0 | 1 | fail, 16 / 310 wrong |
| attention-mask-fill-signoff | claude-opus-5.5 | 2 | fail, 10 / 40 wrong each |
| attention-mask-fill-signoff | claude-opus-5.0 | 1 | fail, 10 / 40 wrong |
| attention-mask-fill-signoff | muse-wonder-full | 1 | fail, 12 / 40 wrong |
| attention-mask-fill-signoff | muse-wonder | 1 | fail, 10 / 40 wrong |
| ram-boot-image | claude-opus-5.5 | 2 | fail, 38 / 256 and 9 / 256 wrong |
| ram-boot-image | claude-fable-5.1 | 2 | fail, 9 / 256 and 38 / 256 wrong |
| never-learned-replies | qwen3.8-27b-fp8 (open weights) | 1 | fail, 310 / 310 wrong (solution skeleton left unimplemented) |
| attention-mask-fill-signoff | qwen3.8-27b-fp8 (open weights) | 1 | fail, no answer file written |
| ram-boot-image | qwen3.8-27b-fp8 (open weights) | 1 | fail, 256 / 256 wrong (no usable estimator) |
Open-weights baseline, reproducible by anyone. The qwen3.8-27b-fp8 rows come from Qwen3.8-27B-FP8 (Apache 2.0) served
with vLLM 0.22.1 on two 24 GB GPUs, driven by Harbor's own terminus-2 agent at temperature 0.2 with the same two-hour
limit. All three runs were stopped by the time limit while still working. The full agent transcript of each run is in
evidence/qwen3.8-27b-fp8/run1/trajectory.json, so the result can be checked line by line and rerun with the command
below.
The evidence/ folder of each task holds, per run, the summary, the grader log with the first failures listed
(expected versus given), the reward file, and the model's deliverable exactly as graded.
Run it yourself
The same three tasks are published on the Harbor registry as fasterlabeling/never-learned-replies,
fasterlabeling/attention-mask-fill-signoff and fasterlabeling/ram-boot-image
(hub.harborframework.com), so they can also be
pulled by name.
pip install harbor
harbor run -p ./never-learned-replies -a oracle # reference solution, expect reward 1
harbor run -p ./never-learned-replies -a terminus-2 -m <your-model>
# the open-weights baseline above, against a local vLLM server:
harbor run -p ./never-learned-replies -a terminus-2 -m openai/<served-name> --ak api_base=http://<host>:8000/v1 --ae OPENAI_API_KEY=none
Any agent that Harbor supports, and any model behind an OpenAI-compatible endpoint, works unchanged. Each task folder
is self-contained: instruction.md, task.toml, environment/Dockerfile, solution/solve.sh, tests/test.sh.
License
Evaluation use is permitted: run any model against these tasks, publish the numbers, cite the set. Use of the tasks,
solutions, graders or transcripts as training data, and redistribution or resale, require written agreement. See
LICENSE.
Built to order
We build tasks like these to order in the same shape, calibrated to a target pass rate on the buyer's model, with grader logs from independent frontier runs delivered with every task. Contact: support@fasterlabeling.com, fasterlabeling.com.