The Shift to Post-Training: How Chinese LLMs Are Transitioning from Basic Capabilities to Verifiable Tasks

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Recent developments in the LLM landscape indicate a strategic pivot from parameter-scaling to post-training optimization. As pre-training yields diminishing returns, the focus has shifted toward large-scale Agent Reinforcement Learning. This article explores how building robust infrastructure—comprising high-concurrency sandboxes, automated evaluation systems, and efficient resource management—enables models to transition from simple question-answering to executing complex, multi-step professional tasks. It also examines how capability transfer from coding to other domains and cost-effective training strategies are driving global competitiveness.

The Diminishing Returns of Pre-Training and the Rise of Post-Training

Over the past two years, the parameter scale arms race served as the primary metric for a model’s potential. When open-source models approached the three-trillion parameter mark, the industry recognized this was nearing the physical limits of current compute clusters and data engineering capabilities. Training such ultra-large-scale models requires not just immense capital, but also systematic engineering capabilities in distributed training architectures, fault tolerance, and data cleaning pipelines.

However, the extensive growth of merely stacking parameters is hitting a bottleneck. The curve of capability improvement is flattening, while inference costs and training energy consumption are climbing exponentially. An industry consensus is emerging: the watershed for next-generation LLMs lies not just in how much data is fed during pre-training, but crucially in how post-training guides models to translate “book knowledge” into “practical skills.” This is akin to training a surgeon—while anatomy textbooks (pre-training) provide the foundational theory, true medical proficiency is determined by countless hands-on drills in a simulated operating room (post-training).

The Core Battlefield of Post-Training: From “Solving Problems” to “Getting Things Done”

The “post-training” emphasized by newer models goes far beyond traditional instruction fine-tuning and human feedback alignment. It is evolving into an infrastructure race centered on “large-scale Agent Reinforcement Learning.”

The root of this shift lies in the differentiated nature of tasks. In traditional evaluations, when a model solves a math problem or answers a factual question, the result is unique with clear judging criteria, making reward signals easy to define. However, in real-world professional scenarios, such as a complete software development task, the model must understand requirements, write code, debug errors, run tests, and iterate based on feedback. This is a multi-step, long-cycle, and dynamic process.

In such complex action sequences, the correctness of each intermediate step cannot be easily labeled with simple binary outcomes. Therefore, building a closed-loop environment that provides “process rewards” becomes crucial. Specifically, this requires the platform to possess the following core capabilities:

High-concurrency sandbox environments: Providing isolated, secure, and easily deployable/destroyable execution environments for thousands of concurrent learning tasks. Models can freely write code and execute commands here without risking real business operations.
Reliable automated evaluation systems: This is the most challenging part. The system must not only determine if the program runs successfully but also evaluate the robustness, maintainability, and execution efficiency of the code. In broader scenarios, this means designing complex evaluation logic to ensure that the “success” triggered by the model is genuine and effective, rather than exploiting loopholes.
Efficient scheduling and resource management: Large-scale reinforcement learning means the model must undergo massive trial-and-error. Each attempt involves resource allocation, task queuing, and state synchronization. The scheduling efficiency of the training framework directly dictates how many valid feedback cycles a model can receive per unit of time, thereby impacting learning convergence speed.

Strategic Transfer: Replicating Coding Capabilities to More Professional Scenarios

The ingenuity of this recent upgrade lies in its explicit proposition of a technical paradigm: “capability transfer” of the reinforcement learning experience accumulated in the coding domain. Programming is a naturally “rule-definitive” field—with compilers as the ultimate arbiter and unit tests as verification standards. By refining in this area, the model learns how to conduct long-range reasoning, correct strategies based on feedback, and search for viable solutions within complex state spaces.

Once acquired, this capability possesses strong generalization potential. For example, in data analysis tasks, the model can similarly perform multi-step operations: querying databases, writing processing scripts, observing outputs, and adjusting query logic based on the results. In complex automated IT operations, the model can simulate troubleshooting workflows—executing monitoring commands, analyzing logs, and fixing configurations—to progressively narrow down the root cause of an issue.

Consequently, the essence of post-training competition is evolving into a contest of “infrastructure density.” The training platform capable of hosting more high-quality professional task environments and designing more precise automated evaluators will be the one to forge the model’s “hands-on capability” at a larger scale and across more diverse dimensions.

Rational Reflections on Intelligence Levels and Cost Structures

Returning to the industry’s focal point: the gap in intelligent experience between this generation of models and top international models is rapidly narrowing, particularly in pure text logical reasoning and code generation. Even more notable is its cost strategy—pricing at merely half of mainstream high-end competitors. This is not a simple price war but a structural advantage derived from technological route choices. If high-quality post-training can unlock greater model potential, the cost per unit of intelligence will be drastically diluted without significantly increasing pre-training compute investments.

This sends a positive competitive signal to the industry. For a long time, accusations regarding “distillation” and “plagiarism” have been a focal point of debate in international tech communities. When models achieve parity with top-tier international standards in specific capability dimensions through a differentiated post-training engineering route, it serves as a robust response to the “scale-only” narrative and “shortcut” accusations. It proves that deep cultivation in algorithmic strategies, data quality, and engineering optimization can also shape globally competitive model capabilities.

Conclusion: The Turning Point from “Generation” to “Delivery”

For developers and enterprise users, the shock and awe of massive parameter counts are fading, replaced by a more pragmatic question: Can it reliably complete the tasks I assign? The post-training orientation of the new generation of LLMs is a direct response to this demand. By introducing reinforcement learning into broader professional toolchains and allowing models to undergo massive trial-and-error in real or high-fidelity simulated environments, we are witnessing the role transformation of LLMs from “smart chatterboxes” to “reliable workers.”