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"Answer the following problem. Explain your reasoning step by step. When you are finished, give your(...TRUNCATED) | 18*(63 - 100 + 82) + 93 | countdown_5arg | countdown | [{"content":"Answer the following problem. Explain your reasoning step by step. When you are finishe(...TRUNCATED) | [] | [] | "{\"metadata\": \"{\\\"source_dataset\\\": \\\"countdown\\\", \\\"source_index\\\": 252, \\\"numbers(...TRUNCATED) | test | "{\"source_dataset\": \"countdown\", \"source_index\": 252, \"numbers\": [100, 93, 82, 18, 63], \"ta(...TRUNCATED) | ["<think>\n<sample>\nTo solve for 903 using the numbers 100, 93, 82, 18, and 63 with each number use(...TRUNCATED) | [
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"Answer the following problem. Explain your reasoning step by step. When you are finished, give your(...TRUNCATED) | 4*7*(77 - 49) - 68 | countdown_5arg | countdown | [{"content":"Answer the following problem. Explain your reasoning step by step. When you are finishe(...TRUNCATED) | [] | [] | "{\"metadata\": \"{\\\"source_dataset\\\": \\\"countdown\\\", \\\"source_index\\\": 253, \\\"numbers(...TRUNCATED) | test | "{\"source_dataset\": \"countdown\", \"source_index\": 253, \"numbers\": [49, 4, 7, 68, 77], \"targe(...TRUNCATED) | ["<think>\n<sample>\nTo solve for 716 using the numbers 49, 4, 7, 68, and 77 with each number used e(...TRUNCATED) | [
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"Answer the following problem. Explain your reasoning step by step. When you are finished, give your(...TRUNCATED) | 96 + 52 + 6 - 93 + 42 | countdown_5arg | countdown | [{"content":"Answer the following problem. Explain your reasoning step by step. When you are finishe(...TRUNCATED) | [] | [] | "{\"metadata\": \"{\\\"source_dataset\\\": \\\"countdown\\\", \\\"source_index\\\": 255, \\\"numbers(...TRUNCATED) | test | "{\"source_dataset\": \"countdown\", \"source_index\": 255, \"numbers\": [96, 6, 42, 93, 52], \"targ(...TRUNCATED) | ["<think>\n<sample>\nTo solve for 103 using the numbers 96, 6, 42, 93, and 52 with each number used (...TRUNCATED) | [
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"Answer the following problem. Explain your reasoning step by step. When you are finished, give your(...TRUNCATED) | 21*36 + 19 + 91 + 66 | countdown_5arg | countdown | [{"content":"Answer the following problem. Explain your reasoning step by step. When you are finishe(...TRUNCATED) | [] | [] | "{\"metadata\": \"{\\\"source_dataset\\\": \\\"countdown\\\", \\\"source_index\\\": 257, \\\"numbers(...TRUNCATED) | test | "{\"source_dataset\": \"countdown\", \"source_index\": 257, \"numbers\": [91, 36, 19, 21, 66], \"tar(...TRUNCATED) | ["<think>\n<sample>\nTo solve for 932 using the numbers 91, 36, 19, 21, and 66 with each number used(...TRUNCATED) | [
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"Answer the following problem. Explain your reasoning step by step. When you are finished, give your(...TRUNCATED) | 7*(97 + 7) - 38 - 34 | countdown_5arg | countdown | [{"content":"Answer the following problem. Explain your reasoning step by step. When you are finishe(...TRUNCATED) | [] | [] | "{\"metadata\": \"{\\\"source_dataset\\\": \\\"countdown\\\", \\\"source_index\\\": 262, \\\"numbers(...TRUNCATED) | test | "{\"source_dataset\": \"countdown\", \"source_index\": 262, \"numbers\": [97, 7, 7, 34, 38], \"targe(...TRUNCATED) | ["<think>\n<sample>\nTo solve for 656 using the numbers 97, 7, 7, 34, and 38 with each number used e(...TRUNCATED) | [
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End of preview. Expand
in Data Studio
Experiment Tracker: FE_8k_ours_cd5arg
Experiment Description: Simple test experiment for Skill Factory workflows.
Start Time: 2025-10-19T03:01:57.145405
Tracker Dataset: TAUR-dev/D-ExpTracker__FE_8k_ours_cd5arg__v1
Stages Completed
Total stages: 1
Models Created
Dataset Configurations
This tracker dataset contains the following configurations with immediate upload as stages complete:
Training Data (Complete Datasets)
Hyperparameters (Complete Configurations)
Logs (Stage-Specific)
Evaluation Results (Complete with Annotations)
Metadata
- experiment_metadata: Timeline and stage information
Usage
Load specific configurations with:
from datasets import load_dataset
# Load experiment metadata
metadata = load_dataset('TAUR-dev/D-ExpTracker__FE_8k_ours_cd5arg__v1', 'experiment_metadata')
# Load complete training datasets
sft_data = load_dataset('TAUR-dev/D-ExpTracker__FE_8k_ours_cd5arg__v1', 'training_data__sft')
sft_metadata = load_dataset('TAUR-dev/D-ExpTracker__FE_8k_ours_cd5arg__v1', 'training_data__sft_metadata')
# Load complete configurations
sft_hyperparams = load_dataset('TAUR-dev/D-ExpTracker__FE_8k_ours_cd5arg__v1', 'hyperparameters__sft')
rl_hyperparams = load_dataset('TAUR-dev/D-ExpTracker__FE_8k_ours_cd5arg__v1', 'hyperparameters__rl')
# Load stage-specific logs
sft_logs = load_dataset('TAUR-dev/D-ExpTracker__FE_8k_ours_cd5arg__v1', 'logs__sft')
rl_logs = load_dataset('TAUR-dev/D-ExpTracker__FE_8k_ours_cd5arg__v1', 'logs__rl')
# Load evaluation results with annotations
sft_eval_results = load_dataset('TAUR-dev/D-ExpTracker__FE_8k_ours_cd5arg__v1', 'evals_eval_sft')
rl_eval_results = load_dataset('TAUR-dev/D-ExpTracker__FE_8k_ours_cd5arg__v1', 'evals_eval_rl')
Models
Registry
All models from this experiment are automatically registered in the SkillFactory Model Registry with:
- Complete training configuration (hyperparameters, datasets, methods)
- Experiment lineage (links back to this tracker dataset)
- Stage-specific metadata (SFT vs RL training details)
- Structured input data references (training datasets and configurations)
Registry entries follow the naming pattern: Model - FE_8k_ours_cd5arg - {stage_name} - {SFT/RL}
Generated by SkillFactory Experiment Management System All artifacts uploaded immediately as stages complete with perfect data provenance
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