id stringclasses 3
values | schema_version int64 2 2 | data_type stringclasses 1
value | loop stringclasses 3
values | language stringclasses 2
values | content_hash stringclasses 3
values | source_kind stringclasses 1
value | source_url stringclasses 1
value | generated_by stringclasses 1
value | generated_at timestamp[s]date 2026-09-20 02:30:26 2026-09-20 09:21:20 | cycle int64 365 367 | focus stringclasses 3
values | goal stringclasses 3
values | tools_offered listlengths 11 11 | steps listlengths 1 1 | num_tool_calls int64 1 1 | outcome stringclasses 1
value | records_written listlengths 0 0 | origin_mix stringclasses 1
value | elapsed_s float64 12.3k 14.8k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
ef537be82a1c528e | 2 | tool_use_trace | reasoning_qa | english | 2136503bafe0a410b31dfd0a | meta-models/Muse-Glimmer-30B | 2026-09-20T02:30:26 | 365 | ml_research | Start now. Focus area: ml_research. Starter queries (adapt freely): speculative decoding; mixture of experts routing.
Already used for this loop (do not reuse): (none yet) | [
"arxiv_search",
"exa_search",
"fetch_url",
"github_code_search",
"github_issues_search",
"github_security_advisories_search",
"hn_search",
"osv_search",
"select_scenario",
"stackexchange_search",
"wikipedia_search"
] | [
{
"assistant_text": "",
"tool_calls": [
{
"name": "arxiv_search",
"arguments_json": "{\"query\": \"speculative decoding\"}",
"origin": "harness"
}
],
"tool_results": [
{
"name": "arxiv_search",
"content": "{\"items\": [{\"title\": \"Speculati... | 1 | no_tool_calls | [] | harness | 14,805.7 | ||
fead52a45162c10d | 2 | tool_use_trace | judgment_dpo | arabic | fe978183df99529a48896eb2 | meta-models/Muse-Glimmer-30B | 2026-09-20T05:55:06 | 366 | credential_secret_handling | Start now. Focus area: credential_secret_handling. Starter queries (adapt freely): api key committed github; secrets in docker image.
The final training text will be written in Arabic, so any source language is fine; wikipedia_search with lang='ar' returns native Arabic material, and technical topics work well when sea... | [
"arxiv_search",
"exa_search",
"fetch_url",
"github_code_search",
"github_issues_search",
"github_security_advisories_search",
"hn_search",
"osv_search",
"select_scenario",
"stackexchange_search",
"wikipedia_search"
] | [
{
"assistant_text": "",
"tool_calls": [
{
"name": "stackexchange_search",
"arguments_json": "{\"query\": \"api key committed github\"}",
"origin": "harness"
}
],
"tool_results": [
{
"name": "stackexchange_search",
"content": "{\"items\": [{\"... | 1 | no_tool_calls | [] | harness | 12,278.5 | ||
580ecf5f255681e6 | 2 | tool_use_trace | sft_helpful | english | 53b709f3625ccdb70e6a75ae | meta-models/Muse-Glimmer-30B | 2026-09-20T09:21:20 | 367 | ml_engineering | Start now. Focus area: ml_engineering. Starter queries (adapt freely): huggingface tokenizer padding; pytorch cuda out of memory.
Already used for this loop (do not reuse): (none yet) | [
"arxiv_search",
"exa_search",
"fetch_url",
"github_code_search",
"github_issues_search",
"github_security_advisories_search",
"hn_search",
"osv_search",
"select_scenario",
"stackexchange_search",
"wikipedia_search"
] | [
{
"assistant_text": "",
"tool_calls": [
{
"name": "stackexchange_search",
"arguments_json": "{\"query\": \"huggingface tokenizer padding\"}",
"origin": "harness"
}
],
"tool_results": [
{
"name": "stackexchange_search",
"content": "{\"items\":... | 1 | no_tool_calls | [] | harness | 12,371.6 |
gijl style dataset (multi-type)
Generated by meta-models/Muse-Glimmer-30B through a tool-using scouting loop over real sources (Stack Exchange, GitHub, OSV, Hacker News, arXiv, Wikipedia, web). Splits are a deterministic hash of the record id (90/5/5); derived records inherit their parent's split. Synthetic, model-written, not human-verified. Every rejected response is intentionally poor and must never be used as an example of good behavior.
| config | folder | train | validation | test | what it is |
|---|---|---|---|---|---|
preference_pair |
dpo_dataset/ |
0 | 0 | 0 | prompt + chosen (calibrated) + rejected (badly calibrated, never operationally harmful) + judgment_rationale, with category / risk_severity / response_pattern. |
sft_chat |
sft_dataset/ |
0 | 0 | 0 | chat-format messages (user/assistant). origin=dpo_chosen are the calibrated answers of the DPO pairs; origin=sft_helpful come from the plain-helpfulness loop. |
reasoning_qa |
reasoning_dataset/ |
0 | 0 | 0 | question + reasoning_steps (list) + answer + confidence, grounded in papers / encyclopedic / discussion material. |
judgment_label |
judgment_labels/ |
0 | 0 | 0 | prompt -> category, risk_severity, response_pattern, rationale. Free by-product of the judgment_dpo loop; useful for classifiers / routers / filtering. |
tool_use_trace |
tool_use_dataset/ |
3 | 0 | 0 | the real tool-calling steps of each scouting cycle (calls, arguments, truncated results, outcome). origin marks model vs harness-run calls. |
source_index |
source_index/ |
0 | 0 | 0 | one row per scenario tried: url, kind, title, length, sha256, status (used/rejected), error. Bodies are NOT stored unless GIJL_STORE_SOURCE_BODIES=1. |
Shared envelope
id, schema_version, data_type, loop, language, content_hash, source_kind, source_url, generated_by, generated_at on every record. content_hash is used for de-duplication.
Loops
judgment_dpo(weight 4): a real request / issue / advisory with a genuine judgment call in it (a risky edge where blind compliance and blanket refusal would both be wrong) -- not a trivial, unambiguous onesft_helpful(weight 3): a real, well-formed technical question or problem where an excellent, accurate, self-contained answer would be valuable (an ordinary helpful-assistant example -- not a risky one)reasoning_qa(weight 2): substantive material (paper abstract, encyclopedia article, technical debate) that supports a question needing multi-step reasoning -- not a trivia lookup
Provenance and licensing
source_index lists the URL and metadata of every scenario tried. Scraped third-party text is not republished by default. Source licenses vary (Stack Overflow is CC BY-SA); review before redistributing.
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