Inside Baidu’s Wenxin 4.5 Turbo & X1 Turbo: Architecture, Training Tricks, and Real-World Impact

Event Overview

On April 25, Baidu held the Create2025 AI Developer Conference in Wuhan, where CEO Robin Li unveiled the Wenxin 4.5 Turbo and X1 Turbo models and several AI applications. CTO Wang Haifeng gave a detailed technical walkthrough.

Model Architecture and Multimodal Capabilities

Wenxin 4.5 is a multimodal foundation model supporting text, image, and video. It uses heterogeneous expert modeling, adaptive‑resolution visual encoding, three‑dimensional rotational positional encoding, and adaptive modality‑aware loss to boost cross‑modal learning efficiency by nearly 2× and improve multimodal understanding by over 30%.

Post‑Training and Reinforcement Learning

Baidu introduced a self‑feedback enhancement framework that creates a “train‑generate‑feedback‑enhance” loop, reducing data production cost and hallucinations while improving complex‑task performance. Preference‑augmented reinforcement learning combines offline preference learning with online RL, unifying reward signals to raise result quality, data efficiency, and training stability.

Deep‑Thinking Chain

The X1 Turbo model incorporates a composite reasoning‑action chain that blends tool calls with chain‑of‑thought prompting, enabling end‑to‑end optimization of both thinking and execution, dramatically expanding cross‑domain problem‑solving ability.

Data Pipeline

A closed‑loop data pipeline—data mining & synthesis → analysis & evaluation → model feedback—continuously generates high‑density, diverse, domain‑wide data, and can be easily transferred to new data types for rapid production.

Wenxin KuaiMa 3.5 (Code Assistant)

Built on Wenxin’s language and code abilities, the code‑agent and intelligent code assistant allow natural‑language driven, multi‑turn application development, supporting no‑code programming. Version 3.5 adds four core capabilities: a multimodal coding engine with tool invocation and preview; a code‑prediction and multi‑line rewrite engine with cursor prediction; an enhanced context engine leveraging Wenxin 4.5 and X1 reasoning; and an open development ecosystem via the MCP protocol. Baidu reports that code generated by KuaiMa now accounts for over 40% of daily new code and has served 7.6 million developers.

PaddlePaddle Joint Optimization

Joint optimizations in the PaddlePaddle framework accelerate multimodal mask attention, reduce token latency with streaming chunk pre‑fill, and cut memory peaks, yielding a 5.4× training throughput increase for Wenxin 4.5 Turbo and an 8× inference throughput boost.

Framework Advances

PaddlePaddle 3.0 continues its “static‑dynamic unified, train‑inference one‑stop” philosophy, adding automatic parallelism (reducing distributed training code by 80%), RL training speed‑up of 114%, high‑order automatic differentiation (ODE solving 115% faster than PyTorch), a neural‑network compiler (27% end‑to‑end speedup), and support for over 60 chip families.

Developer Ecosystem and Cultural Applications

To date, more than 21.85 million developers use the PaddlePaddle‑Wenxin stack, serving over 670 k enterprises and creating 1.1 million models. Baidu also launched a heritage‑focused “non‑intangible cultural heritage martial‑arts” model in partnership with Shanghai Sport University and the China Martial Arts Museum, digitizing techniques via 3D modeling and AI‑driven error correction.

Scholarship Awards

The event concluded with the 12th Baidu Scholarship ceremony, awarding ¥200 k to ten top‑ranked AI researchers from leading universities, underscoring Baidu’s long‑term commitment to AI talent cultivation.