After the release of Step 5 Preview, @StepFun_ai co-founder Yibo Zhu took a closer look at an idea the team has been exploring over the past few months: can training also benefit from “PD separation”? He shared his answer on Zhihu: KV-Invariant Transformer Expansion for Efficient Agentic LLM Scaling—a firsthand technical deep dive into a potential new path for scaling agentic LLMs. 🔗 The core challenge is optimizing three things at once: model quality, training cost, and inference cost. MoE is a classic example of this “three-way win.” But approaches such as Upcycling and YOCO often trade off one dimension against another: scaling them to recover model quality can increase training or inference costs. KITE combines scaling with upcycling, while keeping KV computation invariant. First, a smaller prefiller is trained to generate the KV cache. The model is then expanded with decoder-side components that reuse this KV and continue pretraining. Because these added components do not affect KV computation, the model can scale up to recover quality without proportionally increasing prefill cost. This is particularly relevant in the Agent era, where long-context workloads can make prefill a major part of inference cost. Using a simple Step Scale Transformer (SST), the paper demonstrates a three-way gain over a proportionally scaled classical Transformer baseline: better model quality, lower training cost, and lower inference cost. KITE is ultimately a broader principle of KV-invariant scaling, rather than a single architecture. Better prefiller/decoder designs, KV usage strategies, and scaling laws remain open questions. If agentic workloads continue to be prefill-heavy, KITE could offer an interesting path toward more efficient LLM scaling. KITE has not yet been applied to Step 5, but similar ideas may eventually appear in StepFun’s mainline models. #StepFun# #AI# #Tech# #Scaling# #KITE#