Ran Tao

Black-box RL can boost agent performance by nearly 15 points on complex tasks, revealing a new frontier for scalable optimization.

OPD transfers reasoning skills rather than answers, revealing a complex interplay between teacher-student origins that can either enhance or hinder model capabilities.

Loopie outperforms traditional Transformers by leveraging a novel architecture that maximizes efficiency without sacrificing reasoning power, achieving gold-medal performance in competitive settings.

A two-loop configuration in LoopCoder-v2 boosts code generation performance by over 50% compared to a non-looped baseline, while more loops actually hinder results.

FORT-Searcher achieves superior performance by synthesizing training tasks that actively resist shortcut exploitation, transforming how we train deep search agents.

Building agents that can reliably automate complex, multi-step workflows over local files and tools just got a whole lot easier.

By dynamically injecting frequency-aware n-gram features, X-GRAM achieves state-of-the-art accuracy with smaller embedding tables, offering a practical path to scaling memory-augmented architectures.

Noise-robust visual prompts can improve model performance by over 11% without increasing inference costs.

Industrial code generation gets a reasoning boost: InCoder-32B-Thinking leverages error-driven feedback and a code world model to achieve top-tier performance on complex hardware-aware tasks.

A new 32B code LLM trained specifically for industrial tasks crushes existing models on specialized domains like chip design and GPU kernel optimization, while remaining competitive on general coding benchmarks.

Code LLMs can achieve SOTA performance in agentic tasks by explicitly modeling the dynamic evolution of software logic across different training stages.