Why the AI Race Is Shifting From Pure Reasoning to Actionable Intelligence

From Text Generation to Action‑Oriented Thinking

In the past two years, large language models have evolved from simple text continuation to DeepSeek‑R1‑style reasoning that mimics human deliberation. The next breakthrough, according to former Alibaba Tongyi Qianwen lead Lin Junyang, will be judged by how well models perform in complex real‑world scenarios.

Evolution of Reasoning Models

OpenAI’s o1 model demonstrated that reasoning ability can be a core, independent metric. DeepSeek’s R1 further broke technical barriers by reproducing a post‑training, inference‑driven pipeline at scale. Both approaches treat reasoning as a reinforcement‑learning (RL) problem, training models to think before answering.

Mixed‑Mode Thinking and Its Pain Points

Qwen’s 2025 roadmap envisioned a system that seamlessly blends deep deliberation with rapid instruction following, allowing users to adjust reasoning intensity like a volume knob. In practice, Qwen‑3 introduced a hybrid mode that supports both thoughtful and intuitive behaviors, but integrating the two proved difficult due to divergent data distributions and conflicting reward signals.

Attempts to fuse instruction‑following and reasoning in a single weight file often resulted in compromised performance: instruction responses became verbose and error‑prone, while reasoning paths grew inefficient.

From Static Benchmarks to Dynamic Environments

Traditional RL pipelines treat generated trajectories as independent packets evaluated by simple scorers. Agent‑centric RL now binds the policy network to a complex ecosystem of tool servers, browsers, terminals, search engines, simulators, sandboxes, APIs, memory retrieval systems, and orchestration frameworks. The environment itself becomes a critical research asset, demanding high‑fidelity rules, robust feedback signals, and anti‑cheating safeguards.

Training agents to execute code, interact with tools, and adapt to noisy, incomplete observations requires massive generation throughput, efficient sampling algorithms, and stable strategy‑update mechanisms.

Industry Responses

Anthropic’s Claude 3.7 Sonnet and Claude 4 adopt controllable‑budget hybrid inference, allowing tool calls during deep thinking. GLM‑4.5 and DeepSeek’s later versions also pursue mixed‑mode architectures, but all face the same challenge: ensuring smooth transitions between reasoning and action without degrading performance.

Many organizations now separate instruction‑focused and reasoning‑focused models (e.g., 30B instruction vs. 235B reasoning variants) to avoid the cost and complexity of forced fusion.

Future Direction: Agent‑Centric AI

The field is moving toward training intelligent agents that can plan, act, and continuously adjust strategies in real‑world environments. Success hinges on high‑quality digital environments, precise reward design, robust evaluation pipelines, and secure interfaces between policy networks and external tools.

Ultimately, the most valuable AI systems will be those that translate deep thought into effective action, rather than merely producing longer text outputs. This paradigm shift defines the next era of artificial intelligence.