  Instruction tuning has emerged as the key in aligning large language models
(LLMs) with specific task instructions, thereby mitigating the discrepancy
between the next-token prediction objective and users' actual goals. To reduce
the labor and time cost to collect or annotate data by humans, researchers
start to explore the use of LLMs to generate instruction-aligned synthetic
data. Recent works focus on generating diverse instructions and applying LLM to
increase instruction complexity, often neglecting downstream use cases. It
remains unclear how to tailor high-quality data to elicit better
instruction-following abilities in different target instruction distributions
and LLMs. To this end, we introduce CodecLM, a general framework for adaptively
generating high-quality synthetic data for LLM alignment with different
downstream instruction distributions and LLMs. Drawing on the Encode-Decode
principles, we use LLMs as codecs to guide the data generation process. We
first encode seed instructions into metadata, which are concise keywords
generated on-the-fly to capture the target instruction distribution, and then
decode metadata to create tailored instructions. We also introduce Self-Rubrics
and Contrastive Filtering during decoding to tailor data-efficient samples.
Extensive experiments on four open-domain instruction following benchmarks
validate the effectiveness of CodecLM over the current state-of-the-arts.
