FST framework enables 3x faster LLM adaptation

FST is a paradigm shift that stops trying to force every task nuance into model weights, offloading specialized logic to the context layer instead.

• –Achieving 3x data efficiency makes high-quality RL-style fine-tuning viable for smaller teams with limited compute
• –70% reduction in KL divergence solves the "lobotomy" problem where models lose general reasoning after specialized training
• –Interleaved GEPA (fast loop) and CISPO (slow loop) optimization allows models to acquire new skills like coding and math without interference
• –This multi-channel approach suggests future LLMs will be shipped as "parameter + optimized prompt" bundles rather than static weight files