Satgoy152/Muse-Glimmer-Terminal-Bench-Eval

Muse Glimmer — Terminal-Bench eval traces

This is an EVALUATION set. Do not train on it.

These traces measure baseline speculative-decoding behaviour (acceptance length, draft acceptance rate, throughput) for the Muse Glimmer speculator project on agentic coding work, so that a fine-tuned speculator can be compared against them later. Training data for that project is SWE-Gym and is deliberately repo-disjoint from Terminal-Bench: it excludes every repository referenced by any of the 241 TB tasks. Mixing these traces into training destroys that separation and invalidates the comparison.

The train config name refers to the speculator-training message schema (messages, the field build_speculator_training_dataset() reads), not to a recommendation to train on this data. It exists so the eval set can be rendered by the same code path as the training set.

This dataset is kept in its own repository, separate from the training traces, so that no config-name typo can pull eval data into a training run.

Contents

config	rows	what
train	36	one row per trajectory: the full conversation plus the final assistant reply
raw	1,808	one row per recorded call: the request/response pair as JSON strings, with the scalars worth filtering on promoted to columns

raw is the audit surface. Keep it: the leakage check and any re-tokenization or re-render need the source request and response, not a derived view.

In train, reasoning_strength is also baked into the system message as a Reasoning strength: <level>. sentence. The level lived in extra_body.chat_template_kwargs, which is part of the request and not part of messages, so anything re-rendering from messages alone would otherwise silently apply the chat template's default of high to every row.

request and response in raw stay JSON strings on purpose. A chat-completions body nests tool JSON-Schema to arbitrary depth, and pinning that into a parquet struct would freeze one agent's tool definitions into the dataset schema.

How these were produced

Terminal-Bench 1, original-tasks at commit d28711d0…, on a frozen 40-task subset stratified by difficulty × category (seed 20260830). The subset and the harness that produced these traces are in benchmark/terminal_bench/ in the project repo.

The model was served by vLLM with the official DFlash drafter at 15 speculative tokens and --per-request-spec-decode-metrics summary, which puts a metrics.speculative_decoding block in every response body. That block is preserved verbatim in raw.response, which is what makes acceptance length attributable per request rather than blended across whatever else shares the endpoint. The server-global vllm:spec_decode_* Prometheus counters are not usable for this and are not included.

Four segments, ten tasks each, one per reasoning strength (low, medium, high, xhigh), run at --concurrent 8.

Baseline numbers

Excluding 53 errored calls (a 37-minute endpoint outage; none are model failures) and 2 hand-sent connectivity probes with no recorded reasoning_strength:

strength	calls	out_tok	accept_len	draft_rate	tok/s
low	350	56,664	4.345	0.223	174.2
medium	369	140,552	4.034	0.202	166.4
high	333	132,342	3.796	0.186	155.4
xhigh	701	190,344	3.797	0.186	134.2
pooled	1753	519,902	3.913	0.194	151.1

Acceptance length is pooled as 1 + Σ accepted / Σ steps over the per-request blocks.

Caveats:

• xhigh holds more calls than its share: the GPU was preempted mid-segment and three tasks were re-run, so both the aborted and the completed attempts are present. Pooled acceptance is unaffected — it is a ratio over real generations — but the call counts are not a measure of per-segment effort.
• tok/s is end-to-end per-call throughput under --concurrent 8 and reflects contention as much as the drafter. Compare it only against runs at the same concurrency. Acceptance length is the contention-independent number.
• Acceptance falls as reasoning strength rises.

Task completion: 19/39 resolved (48.7%) — low 5/10, medium 5/10, high 5/10, xhigh 4/9. word2vec-from-scratch is excluded: its image cannot build at the pinned commit (an unpinned huggingface_hub now rejects the Dockerfile's load_dataset('wikitext', …)), so the agent never runs and no trace exists. Any TB1 run fails there today regardless of model.

Canary filtering

Terminal-Bench task Dockerfiles carry a canary GUID (26b5c67b-…). In this run the agent read such a file in two trajectories, and the GUID then persisted in the conversation for every later call in each. Both trajectories were removed in full — 68 calls, 1876 → 1808 — because later calls carry the same tool output in their history. llm-inference-batching-scheduler and amuse-install are therefore absent from these traces, though they still count toward the completion figure above, which is read from the harness's own results files.

Every cell of both configs was scanned for the GUID before upload; the count is zero.

Coverage

Three of the frozen 40 tasks have no trajectory here, for three different reasons, and they should not be conflated:

• llm-inference-batching-scheduler and amuse-install — removed by the canary filter above. They ran, and they still count toward the completion figure.
• word2vec-from-scratch — its image cannot build, so the agent never ran. It is excluded from the completion denominator entirely (resolved: null).
• extract-safely — it produced zero model calls in the whole recording. The task failed before any inference, yet the harness recorded it as an ordinary unresolved task (failure_mode: unset) rather than an agent error, so it counts as a failure in the completion figure without having exercised the model at all.

Two denominators are therefore in play and should be quoted separately: completion is measured over 39 tasks; the traces cover 36 trajectories, of which 38 tasks actually issued model calls.