datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
GPT-5.5-CodexThis dataset was generated using teich by TeichAI
GPT-5.5 Agent traces
This directory contains raw agent trace files generated by teich.
JSONL files: 317
Model metadata: gpt-5.5
Training-ready tools
Generated agent traces carry configured or recovered tool schemas so tools remain available for training even when a session did not call them.
Native Claude Code imports recover schemas for Claude Code and Claude Desktop built-ins, plus conservative name-derived MCP… See the full description on the dataset page: https://huggingface.co/datasets/AletheiaResearch/GPT-5.5-Codex.gpt-5.5-agentThis dataset was generated using teich by TeichAI
Prepare these datasets for supervised fine-tuning in just a few lines of code — see the Conversion section below.
gpt 5.5 Agent Traces
This directory contains raw agent trace files generated by teich. (I also dropped in some of my own personal traces)
All assistant responses were generated by openai/gpt-5.5.
JSONL files: 88
Training-ready tools
A complete configured tools schema snapshot is embedded in the… See the full description on the dataset page: https://huggingface.co/datasets/armand0e/gpt-5.5-agent.GPT-5.6-Sol-Luna-Terra-Traces
GPT-5.6 — Sol · Terra · Luna Library
A maintained mirror of every GPT-5.6 Sol / Terra / Luna dataset on Hugging Face — content-verified, attributed, in one place.
Dataset Viewer | Parquet
// what this is
This is a maintained library — a community mirror of every publicly-available GPT-5.6 Sol / Terra / Luna dataset on Hugging Face, aggregated, validity-filtered, and content-verified with per-row source attribution. It is not Crownelius' own data. Every row… See the full description on the dataset page: https://huggingface.co/datasets/Crownelius/GPT-5.6-Sol-Luna-Terra-Traces.SPADE-Environment-Pool-GPT5.5-ToolUse
SPARE GPT-5.5 Multi-Turn Tool-Use Games v1
A public static pool of 11,039 validated multi-turn tool-use environments generated by GPT-5.5 for SPARE actor training.
Training alignment
Source recipe: Qwen3-30B-A3B 0624 tool-use GAMES configuration
400 rollouts x 24 games/rollout = 9,600 no-reuse games required
11,039 validated games provide 1,439 games of headroom
Six balanced skills: API orchestration, data retrieval, state modification, error recovery, tool… See the full description on the dataset page: https://huggingface.co/datasets/spade-rl/SPADE-Environment-Pool-GPT5.5-ToolUse.gpt-5.5-agentThis dataset was generated using teich by TeichAI
Prepare these datasets for supervised fine-tuning in just a few lines of code — see the Conversion section below.
gpt 5.5 Agent Traces
This directory contains raw agent trace files generated by teich. (I also dropped in some of my own personal traces)
All assistant responses were generated by openai/gpt-5.5.
JSONL files: 88
Training-ready tools
A complete configured tools schema snapshot is embedded in the… See the full description on the dataset page: https://huggingface.co/datasets/nphearum/gpt-5.5-agent.new_audit_gpt54mini_claude46_k493_n200_b005harvey-eval-gpt56sol-qwen35-9b-notes70-recall30-30m-historical-20t-think
harvey-eval-gpt56sol-qwen35-9b-notes70-recall30-30m-historical-20t-think
1,000 historical evaluation attempts (250 tasks, four samples per task), newly
graded with gpt-5.6-sol using Harvey's original per-criterion rubric prompt
and all-criteria-pass rule. Mean all-pass rate: 5.0000%.
The train split contains evaluation records, not training examples.
Generation and grading protocols
Generation is unchanged: historical 20-turn thinking-enabled
glob/grep/read agent… See the full description on the dataset page: https://huggingface.co/datasets/violetxi/harvey-eval-gpt56sol-qwen35-9b-notes70-recall30-30m-historical-20t-think.harvey-eval-gpt56sol-qwen35-9b-notes70-recall30-3m-historical-20t-think
harvey-eval-gpt56sol-qwen35-9b-notes70-recall30-3m-historical-20t-think
1,000 historical evaluation attempts (250 tasks, four samples per task), newly
graded with gpt-5.6-sol using Harvey's original per-criterion rubric prompt
and all-criteria-pass rule. Mean all-pass rate: 1.3000%.
The train split contains evaluation records, not training examples.
Generation and grading protocols
Generation is unchanged: historical 20-turn thinking-enabled
glob/grep/read agent… See the full description on the dataset page: https://huggingface.co/datasets/violetxi/harvey-eval-gpt56sol-qwen35-9b-notes70-recall30-3m-historical-20t-think.harvey-eval-gpt56sol-qwen35-9b-notes70-recall30-10m-historical-20t-think
harvey-eval-gpt56sol-qwen35-9b-notes70-recall30-10m-historical-20t-think
1,000 historical evaluation attempts (250 tasks, four samples per task), newly
graded with gpt-5.6-sol using Harvey's original per-criterion rubric prompt
and all-criteria-pass rule. Mean all-pass rate: 4.0000%.
The train split contains evaluation records, not training examples.
Generation and grading protocols
Generation is unchanged: historical 20-turn thinking-enabled
glob/grep/read agent… See the full description on the dataset page: https://huggingface.co/datasets/violetxi/harvey-eval-gpt56sol-qwen35-9b-notes70-recall30-10m-historical-20t-think.gpt-5.5-agentThis dataset was generated using teich by TeichAI
Prepare these datasets for supervised fine-tuning in just a few lines of code — see the Conversion section below.
gpt 5.5 Agent Traces
This directory contains raw agent trace files generated by teich. (I also dropped in some of my own personal traces)
All assistant responses were generated by openai/gpt-5.5.
JSONL files: 88
Training-ready tools
A complete configured tools schema snapshot is embedded in the… See the full description on the dataset page: https://huggingface.co/datasets/1izailizai1m1/gpt-5.5-agent.gpt-5.5-agentThis dataset was generated using teich by TeichAI
Prepare these datasets for supervised fine-tuning in just a few lines of code — see the Conversion section below.
gpt 5.5 Agent Traces
This directory contains raw agent trace files generated by teich. (I also dropped in some of my own personal traces)
All assistant responses were generated by openai/gpt-5.5.
JSONL files: 88
Training-ready tools
A complete configured tools schema snapshot is embedded in the… See the full description on the dataset page: https://huggingface.co/datasets/Quaxicron/gpt-5.5-agent.hle-context-baseline-gpt55gpt-5.4-step-by-step-reasoning
Dataset Card for GPT-5.4-Reasoning-1500-Ultra-Logic
Dataset Details
Dataset Description
Suggestion: I would use this to fine-tune qwen3.5 35b a3b moe, or 27b variant. However, for maximum efficiency, 2bb-20b LLMs like qwen3.5 9b and 4b, gpt-oss 20b work perfectly. Fine-tuning the newest versions (specialized reasoning variants) will yield the most significant logic jumps.
This dataset is an ultra-high-density synthetic reasoning corpus containing… See the full description on the dataset page: https://huggingface.co/datasets/Roman1111111/gpt-5.4-step-by-step-reasoning.gpt-5-mini-rebench-v2-cppgpt-5-6-sol-coding-and-debugging-traces
Mirror: greghavens/gpt-5.6-sol-coding-and-debugging-traces
Pinned snapshot / mirror of greghavens/gpt-5.6-sol-coding-and-debugging-traces, re-hosted for PROTISEC
research reproducibility. Redistributed under the upstream license (cc-by-4.0)
with attribution — all credit to the original author.
Original author: greghavens
Source dataset: greghavens/gpt-5.6-sol-coding-and-debugging-traces
License: cc-by-4.0
Family: coding_traces
Mode: stream
Rows cached: 17939
Changes vs… See the full description on the dataset page: https://huggingface.co/datasets/ansulev/gpt-5-6-sol-coding-and-debugging-traces.cql_gen-browsecomp_plus_qa_gen-oai_gpt5_low-multihop_2-v3187harvey-eval-gpt56sol-qwen35-9b-notes70-recall30-1m-historical-20t-think
harvey-eval-gpt56sol-qwen35-9b-notes70-recall30-1m-historical-20t-think
1,000 historical evaluation attempts (250 tasks, four samples per task), newly
graded with gpt-5.6-sol using Harvey's original per-criterion rubric prompt
and all-criteria-pass rule. Mean all-pass rate: 2.2000%.
The train split contains evaluation records, not training examples.
Generation and grading protocols
Generation is unchanged: historical 20-turn thinking-enabled
glob/grep/read agent… See the full description on the dataset page: https://huggingface.co/datasets/violetxi/harvey-eval-gpt56sol-qwen35-9b-notes70-recall30-1m-historical-20t-think.indist-tool-v0-pool-v2-gpt55-1k_issue_rewritten_prompt-v3-v5_swesmith_metadata_repairedgpt-5-mini-rebench-v2-calfworld-experimenter-gpt5mini-sft-1k
ALFWorld Experimenter GPT-5 mini SFT 1K
This dataset contains 1,000 blind GPT-5 mini reasoning demonstrations for an
ALFWorld expert-prefix selection task. The intended use is to give a 7B
experimenter model a structured reasoning warm start before reinforcement
learning, not to treat GPT-5 mini's selected depths as ground-truth labels.
Task
For each ALFWorld task, the experimenter receives eight failed trajectories
from a frozen Qwen2.5-7B-Instruct actor and one… See the full description on the dataset page: https://huggingface.co/datasets/YYYYYYibo/alfworld-experimenter-gpt5mini-sft-1k.GPT-5.6-Sol-Luna-Terra-Traces
GPT-5.6 — Sol · Terra · Luna Library
A maintained mirror of every GPT-5.6 Sol / Terra / Luna dataset on Hugging Face — content-verified, attributed, in one place.
Dataset Viewer | Parquet
// what this is
This is a maintained library — a community mirror of every publicly-available GPT-5.6 Sol / Terra / Luna dataset on Hugging Face, aggregated, validity-filtered, and content-verified with per-row source attribution. It is not Crownelius' own data. It exists to… See the full description on the dataset page: https://huggingface.co/datasets/Manusagents/GPT-5.6-Sol-Luna-Terra-Traces.arc-agi-3-schema-traces-gpt56
ARC-AGI-3 Schema Gameplay Trajectories — GPT-5.6 Sol
This release contains every gpt-5.6-sol gameplay trajectory produced on our
cluster with the world_model_v5 agent harness — 100 runs across the 25 public
ARC-AGI-3 games — plus a dependency-free scoring utility.
It is the GPT-5.6 Sol member of a family built by the same harness and the same
sanitizer, so trajectories can be compared game by game:
arc-agi-3-schema-traces-fable5 — Claude Fable 5, best per game (25)… See the full description on the dataset page: https://huggingface.co/datasets/guanning/arc-agi-3-schema-traces-gpt56.patents-classified-2106-gpt5-miniBIRD-Verified-CoT-2462-GPT5.4
BIRD-Verified-CoT-2462 (GPT-5.4 distilled)
Likely the first publicly available CoT-augmented Text-to-SQL dataset built on top of expert-verified BIRD data.
This dataset combines two state-of-the-art ingredients:
ReViSQL's BIRD-Verified subset — 2,462 SQL-expert verified examples (multi-round review by UIUC team), eliminating the ~50% annotation noise of the original BIRD train set.
GPT-5.4 (via Codex CLI) — distilled into structured 6-section Chain-of-Thought traces using… See the full description on the dataset page: https://huggingface.co/datasets/wenyupapa/BIRD-Verified-CoT-2462-GPT5.4.tis-movedist-gpt54
TIS move+distance SFT trajectories — gpt-5.4 teacher
Explore-then-answer supervised-fine-tuning trajectories for thinking-in-space 3D spatial
reasoning. An agent is shown one camera view of an indoor scene, calls a discrete move tool
(forward/backward/left/right/up/down/turn_*, with an optional distance) to gather evidence over
locally ray-cast dense-mesh renders, then answers. This is the v10 move+distance convention
(room dimensions given in the first turn; no scene id in the… See the full description on the dataset page: https://huggingface.co/datasets/Icey444/tis-movedist-gpt54.gpt-5.4-step-by-step-reasoning
Dataset Card for GPT-5.4-Reasoning-1500-Ultra-Logic
Dataset Details
Dataset Description
Suggestion: I would use this to fine-tune qwen3.5 35b a3b moe, or 27b variant. However, for maximum efficiency, 2bb-20b LLMs like qwen3.5 9b and 4b, gpt-oss 20b work perfectly. Fine-tuning the newest versions (specialized reasoning variants) will yield the most significant logic jumps.
This dataset is an ultra-high-density synthetic reasoning corpus containing… See the full description on the dataset page: https://huggingface.co/datasets/ansulev/gpt-5.4-step-by-step-reasoning.harvey-eval-gpt56sol-qwen35-9b-base-20t-think
harvey-eval-gpt56sol-qwen35-9b-base-20t-think
1,000 historical evaluation attempts (250 tasks, four samples per task), newly
graded with gpt-5.6-sol using Harvey's original per-criterion rubric prompt
and all-criteria-pass rule. Mean all-pass rate: 3.9000%.
The train split contains evaluation records, not training examples.
Generation and grading protocols
Generation is unchanged: historical 20-turn thinking-enabled
glob/grep/read agent runs. These are not new… See the full description on the dataset page: https://huggingface.co/datasets/violetxi/harvey-eval-gpt56sol-qwen35-9b-base-20t-think.gpt-5.4-step-by-step-reasoning
Dataset Card for GPT-5.4-Reasoning-1500-Ultra-Logic
Dataset Details
Dataset Description
Suggestion: I would use this to fine-tune qwen3.5 35b a3b moe, or 27b variant. However, for maximum efficiency, 2bb-20b LLMs like qwen3.5 9b and 4b, gpt-oss 20b work perfectly. Fine-tuning the newest versions (specialized reasoning variants) will yield the most significant logic jumps.
This dataset is an ultra-high-density synthetic reasoning corpus containing… See the full description on the dataset page: https://huggingface.co/datasets/invincible-jha/gpt-5.4-step-by-step-reasoning.harvey-eval-gpt56sol-qwen35-9b-notes70-notecondtraj30-1m-historical-20t-think
harvey-eval-gpt56sol-qwen35-9b-notes70-notecondtraj30-1m-historical-20t-think
1,000 historical evaluation attempts (250 tasks, four samples per task), graded
with gpt-5.6-sol using Harvey's original per-criterion rubric prompt and
binary all-criteria-pass rule. Mean all-pass rate: 3.1000%.
The train split contains held-out evaluation records, not training examples.
Model and training mixture
The evaluated checkpoint is Qwen3.5-9B trained for two epochs on the… See the full description on the dataset page: https://huggingface.co/datasets/violetxi/harvey-eval-gpt56sol-qwen35-9b-notes70-notecondtraj30-1m-historical-20t-think.harvey-eval-gpt56sol-qwen35-9b-notes70-notecondtraj30-5m-historical-20t-think
harvey-eval-gpt56sol-qwen35-9b-notes70-notecondtraj30-5m-historical-20t-think
1,000 historical evaluation attempts (250 tasks, four samples per task), graded
with gpt-5.6-sol using Harvey's original per-criterion rubric prompt and
binary all-criteria-pass rule. Mean all-pass rate: 3.4000%.
The train split contains held-out evaluation records, not training examples.
Model and training mixture
The evaluated checkpoint is Qwen3.5-9B trained for two epochs on the… See the full description on the dataset page: https://huggingface.co/datasets/violetxi/harvey-eval-gpt56sol-qwen35-9b-notes70-notecondtraj30-5m-historical-20t-think.