data_source stringclasses 1
value | prompt listlengths 2 2 | ability stringclasses 1
value | reward_model dict | extra_info dict |
|---|---|---|---|---|
datacuration | [
{
"role": "system",
"content": "You are a **training-data curator**. A small base language model needs to do better on a **target task**, and you must construct the best possible fine-tuning set for it. The model will be LoRA-fine-tuned on exactly what you submit, then its **perplexity on held-out target da... | datacuration_selection | {
"style": "datacuration",
"ground_truth": "{\"task_id\": \"lang_de\", \"budget\": 2048, \"token_budget\": 131072, \"ppl_base\": 18.808332693182507, \"nll_base\": 2.9343, \"eval_ref\": \"lang_de\", \"oracle_delta\": 0.0931, \"random_delta\": 0.031}"
} | {
"index": "lang_de",
"uid": "lang_de",
"axis": "lang",
"budget": 2048,
"token_budget": 131072,
"variant": "curate"
} |
datacuration | [
{
"role": "system",
"content": "You are a **training-data curator**. A small base language model needs to do better on a **target task**, and you must construct the best possible fine-tuning set for it. The model will be LoRA-fine-tuned on exactly what you submit, then its **perplexity on held-out target da... | datacuration_selection | {
"style": "datacuration",
"ground_truth": "{\"task_id\": \"lang_es\", \"budget\": 2048, \"token_budget\": 131072, \"ppl_base\": 20.805138511102886, \"nll_base\": 3.0352, \"eval_ref\": \"lang_es\", \"oracle_delta\": 0.1592, \"random_delta\": 0.0278}"
} | {
"index": "lang_es",
"uid": "lang_es",
"axis": "lang",
"budget": 2048,
"token_budget": 131072,
"variant": "curate"
} |
datacuration | [
{
"role": "system",
"content": "You are a **training-data curator**. A small base language model needs to do better on a **target task**, and you must construct the best possible fine-tuning set for it. The model will be LoRA-fine-tuned on exactly what you submit, then its **perplexity on held-out target da... | datacuration_selection | {
"style": "datacuration",
"ground_truth": "{\"task_id\": \"lang_fr\", \"budget\": 2048, \"token_budget\": 131072, \"ppl_base\": 18.957506984077874, \"nll_base\": 2.9422, \"eval_ref\": \"lang_fr\", \"oracle_delta\": 0.1056, \"random_delta\": 0.0323}"
} | {
"index": "lang_fr",
"uid": "lang_fr",
"axis": "lang",
"budget": 2048,
"token_budget": 131072,
"variant": "curate"
} |
datacuration | [
{
"role": "system",
"content": "You are a **training-data curator**. A small base language model needs to do better on a **target task**, and you must construct the best possible fine-tuning set for it. The model will be LoRA-fine-tuned on exactly what you submit, then its **perplexity on held-out target da... | datacuration_selection | {
"style": "datacuration",
"ground_truth": "{\"task_id\": \"lang_hi\", \"budget\": 2048, \"token_budget\": 131072, \"ppl_base\": 4.692192700684559, \"nll_base\": 1.5459, \"eval_ref\": \"lang_hi\", \"oracle_delta\": 0.0533, \"random_delta\": 0.0069}"
} | {
"index": "lang_hi",
"uid": "lang_hi",
"axis": "lang",
"budget": 2048,
"token_budget": 131072,
"variant": "curate"
} |
datacuration | [
{
"role": "system",
"content": "You are a **training-data curator**. A small base language model needs to do better on a **target task**, and you must construct the best possible fine-tuning set for it. The model will be LoRA-fine-tuned on exactly what you submit, then its **perplexity on held-out target da... | datacuration_selection | {
"style": "datacuration",
"ground_truth": "{\"task_id\": \"lang_ja\", \"budget\": 2048, \"token_budget\": 131072, \"ppl_base\": 6.237004381219701, \"nll_base\": 1.8305, \"eval_ref\": \"lang_ja\", \"oracle_delta\": 0.0488, \"random_delta\": 0.0069}"
} | {
"index": "lang_ja",
"uid": "lang_ja",
"axis": "lang",
"budget": 2048,
"token_budget": 131072,
"variant": "curate"
} |
datacuration | [
{
"role": "system",
"content": "You are a **training-data curator**. A small base language model needs to do better on a **target task**, and you must construct the best possible fine-tuning set for it. The model will be LoRA-fine-tuned on exactly what you submit, then its **perplexity on held-out target da... | datacuration_selection | {
"style": "datacuration",
"ground_truth": "{\"task_id\": \"lang_ko\", \"budget\": 2048, \"token_budget\": 131072, \"ppl_base\": 3.7344479383893328, \"nll_base\": 1.3176, \"eval_ref\": \"lang_ko\", \"oracle_delta\": 0.0277, \"random_delta\": 0.0056}"
} | {
"index": "lang_ko",
"uid": "lang_ko",
"axis": "lang",
"budget": 2048,
"token_budget": 131072,
"variant": "curate"
} |
datacuration | [
{
"role": "system",
"content": "You are a **training-data curator**. A small base language model needs to do better on a **target task**, and you must construct the best possible fine-tuning set for it. The model will be LoRA-fine-tuned on exactly what you submit, then its **perplexity on held-out target da... | datacuration_selection | {
"style": "datacuration",
"ground_truth": "{\"task_id\": \"lang_ru\", \"budget\": 2048, \"token_budget\": 131072, \"ppl_base\": 5.497536044769496, \"nll_base\": 1.7043, \"eval_ref\": \"lang_ru\", \"oracle_delta\": 0.0443, \"random_delta\": 0.0038}"
} | {
"index": "lang_ru",
"uid": "lang_ru",
"axis": "lang",
"budget": 2048,
"token_budget": 131072,
"variant": "curate"
} |
datacuration | [
{
"role": "system",
"content": "You are a **training-data curator**. A small base language model needs to do better on a **target task**, and you must construct the best possible fine-tuning set for it. The model will be LoRA-fine-tuned on exactly what you submit, then its **perplexity on held-out target da... | datacuration_selection | {
"style": "datacuration",
"ground_truth": "{\"task_id\": \"lang_tr\", \"budget\": 2048, \"token_budget\": 131072, \"ppl_base\": 20.583710282662036, \"nll_base\": 3.0245, \"eval_ref\": \"lang_tr\", \"oracle_delta\": 0.1178, \"random_delta\": 0.0309}"
} | {
"index": "lang_tr",
"uid": "lang_tr",
"axis": "lang",
"budget": 2048,
"token_budget": 131072,
"variant": "curate"
} |
datacuration | [
{
"role": "system",
"content": "You are a **training-data curator**. A small base language model needs to do better on a **target task**, and you must construct the best possible fine-tuning set for it. The model will be LoRA-fine-tuned on exactly what you submit, then its **perplexity on held-out target da... | datacuration_selection | {
"style": "datacuration",
"ground_truth": "{\"task_id\": \"lang_vi\", \"budget\": 2048, \"token_budget\": 131072, \"ppl_base\": 7.50372827229365, \"nll_base\": 2.0154, \"eval_ref\": \"lang_vi\", \"oracle_delta\": 0.0803, \"random_delta\": 0.012}"
} | {
"index": "lang_vi",
"uid": "lang_vi",
"axis": "lang",
"budget": 2048,
"token_budget": 131072,
"variant": "curate"
} |
datacuration | [
{
"role": "system",
"content": "You are a **training-data curator**. A small base language model needs to do better on a **target task**, and you must construct the best possible fine-tuning set for it. The model will be LoRA-fine-tuned on exactly what you submit, then its **perplexity on held-out target da... | datacuration_selection | {
"style": "datacuration",
"ground_truth": "{\"task_id\": \"mmlu_college_mathematics\", \"budget\": 2048, \"token_budget\": 131072, \"ppl_base\": 11.552478468906273, \"nll_base\": 2.4469, \"eval_ref\": \"mmlu_college_mathematics\", \"oracle_delta\": 0.4414, \"random_delta\": 0.3176}"
} | {
"index": "mmlu_college_mathematics",
"uid": "mmlu_college_mathematics",
"axis": "mmlu",
"budget": 2048,
"token_budget": 131072,
"variant": "curate"
} |
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