SGLang adds Jev-style decision-making modes to standard large language models, allowing them to make direct selections without fine-tuning.
Beating AI Insight News Brief: Large model inference and deployment framework SGLang has added native decision-making APIs, enabling off-the-shelf LLMs and multimodal models like Qwen3.8-27B to directly perform selection, judgment, and scoring without modifying weights or undergoing retraining. When developers input the current context and several candidate answers, the model directly returns the probability of each option, eliminating the need to first generate a text segment for subsequent parsing. This functionality leverages the "next-token probability" that large language models inherently compute. For example, if candidate answers are A, B, and C, a standard chat model would continue generating text, while SGLang directly reads the model’s probabilities for these options to determine its preferred choice. The model itself remains unchanged; only the method of reading outputs is adjusted.
SGLang has demonstrated this capability with a *Pokémon FireRed* demo using Qwen3.8-27B. The model decides whether to attack, switch Pokémon, or heal based on real-time game state, with each decision taking less than 100ms, and successfully cleared the Elite Four and Champion in one run. Since Qwen3.8-27B natively supports image input, this approach can also handle multimodal decision-making.
Additionally, SGLang has added a new /v1/systemone endpoint, compatible with the TypeSafe SDK used by Jev. Applications already using this SDK can switch their service address to their own SGLang instance to continue making calls. The new feature is now available in the nightly build and scheduled for official release alongside version v0.5.21.
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