SJY23/PiKa-SFT-30k

PiKa Dataset

Official dataset for:

PIKA: Expert-Level Synthetic Datasets for Post-Training Alignment from Scratch

PiKa is a 30K GPT-4o-generated expert-level dataset for post-training alignment.

Data Format

Each example contains:

• instruction
• chosen

Results

Table 1

Prompt difficulty comparison on AlpacaEval 2. We compare PiKa variants with different difficulty levels and show that the expert setting delivers the strongest alignment performance.

Dataset	Difficulty	AlpacaEval 2 LC (%)	WR (%)
MAGPIE-Pro	2.65	15.42	16.89
PiKa-Series (10K Subset), w/o Persona-Guide	3.11	13.84	15.53
PiKa-Series (10K Subset), Low-Diff	2.91	21.86	14.95
PiKa-Series (10K Subset), Mid-Diff	3.64	24.36	17.84
PiKa-Series (10K Subset), Expert (Default)	7.39	31.01	30.32

Table 2

Performance comparison of instruction-tuned models based on Llama-3-8B-Base using PiKa-generated versus baseline datasets. PiKa achieves superior performance while requiring 10x less training data than state-of-the-art MAGPIE methods.

Alignment Setup (Base LLM = Llama-3-8B-Base)	#Convs	AlpacaEval 2 LC (%)	Arena-Hard WR (%)
Llama-3-8B-Instruct (Official)	>10M	28.36	24.5
Self-Instruct (Llama-3) (Wang et al., 2023)	100K	8.86	3.3
ShareGPT (Chiang et al., 2023)	112K	6.98	6.9
Ultrachat (Ding et al., 2023)	208K	6.70	3.6
OpenHermes 1 (Teknium, 2023a)	243K	8.69	5.3
Tulu V2 Mix (Ivison et al., 2023)	326K	10.95	6.3
WildChat (Zhao et al., 2024)	652K	14.75	11.7
OpenHermes 2.5 (Teknium, 2023b)	1M	12.40	7.7
MAGPIE-Air-300K-Filtered (Xu et al., 2025)	300K	25.24	20.7
MAGPIE-Pro-300K-Filtered (Xu et al., 2025)	300K	24.06	23.9
PiKa (Ours)	30K	32.82	33.5

Table 3

Performance comparison on additional downstream objective tasks from the Open LLM Leaderboard. The goal of this evaluation is to assess whether alignment with PiKa preserves performance on objective tasks rather than optimizing only for alignment benchmarks. All models are supervised fine-tuned on Llama-3-8B-Base. Numbers in parentheses indicate the number of few-shot examples.

Alignment Setup	MMLU (5)	ARC (25)	HellaSwag (10)	TruthfulQA (0)	WinoGrande (5)	GSM8K (5)	Average
Llama-3-8B-Instruct	67.82	61.52	78.67	52.47	72.14	71.72	67.39
ShareGPT	66.03	58.45	81.50	52.34	74.03	48.67	63.50
OpenHermes 1	65.42	62.29	82.15	50.85	75.61	47.16	63.58
OpenHermes 2.5	65.70	61.86	82.53	51.35	76.09	67.02	67.09
Tulu V2 Mix	66.34	59.22	82.80	47.99	76.16	58.07	65.10
WildChat	65.95	59.22	81.39	53.18	75.30	48.75	63.97
UltraChat	65.23	62.12	81.68	52.76	75.53	50.57	64.65
MAGPIE-Air-300K-Filtered	64.45	61.01	79.90	53.48	72.38	52.24	63.58
MAGPIE-Pro-300K-Filtered	64.25	60.41	80.52	52.46	73.32	47.92	63.15
PiKa	62.85	59.98	80.02	52.48	73.01	52.84	63.53

Citation

If you use this dataset, please cite our paper:

@misc{yin2025pikaexpertlevelsyntheticdatasets,
      title={PIKA: Expert-Level Synthetic Datasets for Post-Training Alignment from Scratch},
      author={Shangjian Yin and Shining Liang and Wenbiao Ding and Yuli Qian and Zhouxing Shi and Hongzhi Li and Yutao Xie},
      year={2025},
      eprint={2510.06670},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2510.06670},
}