direct-5d8b7cb8·2 events·first seen Aliases: DIRECT
DIRECT is a proposed framework for sequence labeling that applies Direct Preference Optimization (DPO) after supervised fine-tuning to improve task alignment, then uses a controlled decoding process that enforces fixed output formats and restricts predictions to candidate label sets. A template-filling mechanism with KV Cache reuse reduces redundant computation by generating only label tokens. Experiments across eight datasets show improvements in both accuracy and inference efficiency over existing LLM-based sequence labeling methods.
Researchers introduce DIRECT, a routing framework that dynamically allocates test-time compute for Vision-Language Models acting as embodied planners, using multimodal scene context to decide per-prompt how much compute to spend. Experiments on VLABench and RoboMME benchmarks show that different scaling axes (chain-of-thought depth, model size, memory history) yield qualitatively distinct gains, and that naive uniform scaling is wasteful. On a physical Franka arm, DIRECT matches or exceeds a stronger model's success rate at up to 65% lower average latency, improving the success-cost Pareto frontier.