Why Pre‑training Teams Boost New Engineers’ Skills Faster Than SFT Teams

For fresh graduates entering AI model development, the most urgent goal is to improve engineering and coding capabilities. The author compares two typical teams—pre‑training and SFT (Supervised Fine‑Tuning)—and outlines the core tasks each requires.

Core Responsibilities in Pre‑training Teams

Newcomers must handle large‑scale data collection, cleaning, and deduplication using tools such as Hadoop and Spark; set up various Torch and CUDA environments; understand, modify, and optimize Megatron code; debug multi‑node communication errors; and master “alchemy” techniques like data balancing, learning‑rate schedules, optimizer choices, loss‑curve analysis, and scaling laws. They also run benchmark tests to verify model performance.

Core Responsibilities in SFT Teams

In contrast, SFT teams treat training frameworks and advanced tuning as optional. Mandatory tasks focus on data annotation: manually labeling data, using GPT‑4 to assist labeling, training annotators, incorporating user feedback, refining data based on experiments, and sometimes synthesizing data from research papers.

Transition Difficulty Between Teams

Because pre‑training engineers already work with training code, moving to SFT requires only a brief familiarization with data, allowing them to start quickly. However, SFT engineers shifting to pre‑training must spend weeks learning Megatron and distributed training infrastructure before contributing.

Messy Work that Shapes Skills

Pre‑training “messy work” involves extracting clean training data from massive, noisy web sources, building small models to score data, writing extensive filtering rules, and analyzing domain‑specific data characteristics.

SFT “messy work” mainly consists of manual data labeling and iteratively crafting prompts for GPT‑4 to achieve the best labeling results.

Advice for Newcomers

The author argues that newcomers should tackle the most challenging tasks—pre‑training work—to rapidly develop solid engineering fundamentals. Although pre‑training may lack short‑term visible results and face long‑term model training hurdles, it offers valuable experience that protects against future career stagnation.

Even seasoned programmers familiar with Hadoop and BERT can benefit from occasional SFT projects to understand large‑model behavior, but the foundational skill set is best built in pre‑training environments.

Final Takeaway

Regardless of the chosen team, actively seek mentorship, ask questions, and engage with senior colleagues to accelerate learning; the effort invested in pre‑training tasks pays off in long‑term engineering competence.