Post-Training Evolution of Chinese Foundation Models: From Code Generation to 3D Modeling and General-Purpose Capability Gains

Background: Post-Training Becomes the Main Arena for Capability Advancement

Over the past year, the competitive focus in large language models has shifted from “pre-training scale” to “post-training quality.” Pre-training determines a model’s knowledge breadth and foundational reasoning ability, while post-training—including supervised fine-tuning, preference alignment, reinforcement learning, and domain specialization—directly determines usability and stability in real-world tasks.

A clear trend has emerged: leading models are no longer iterating through major generational leaps but rather through refined improvements within the same version. After a round of high-quality post-training, the same base model can show significant gains on specific tasks, sometimes approaching or surpassing larger models on narrow benchmarks. This “small-step, fast-iteration” approach means shorter wait times and smoother upgrade paths for developers.

This article uses recent post-training capability evolution in Chinese models as a starting point, focusing on three high-value scenarios: code generation, 3D modeling, and financial document reasoning. It analyzes technical routes, capability boundaries, and deployment practices. Note that this discussion covers general technical capabilities and engineering practices; it does not recommend or evaluate any specific vendor’s products.

Code Capability: Terminal Tasks Become the New Benchmark

Why Terminal Tasks Are Harder Than Code Completion

Traditional code benchmarks focus on function-level completion or algorithm problem solving—tasks with closed contexts, clear constraints, and well-defined grading criteria. But real development scenarios often involve code tasks in terminal environments: understanding file system state, executing commands, parsing output, adjusting strategy based on error messages, and maintaining goal consistency across multiple interactions.

These tasks demand more from the model:

• Environment awareness: understanding the current working directory, dependency state, and permission constraints;
• Multi-step planning: breaking vague requirements into executable command sequences;
• Error recovery: reading error output after a failed command and correcting course, rather than repeating ineffective actions;
• Cost control: reducing interaction rounds and token consumption while maintaining success rates.

How Post-Training Improves Terminal Task Performance

From a technical standpoint, improving terminal task performance typically relies on three post-training methods:

1. Trajectory-level supervised fine-tuning: collecting high-quality human operation traces or successful trajectories generated by expert systems, allowing the model to learn the mapping between “state-action-result”;
2. Verifiable reward reinforcement learning: using task completion itself as the reward signal rather than matching reference answers, encouraging the model to explore more robust execution paths;
3. Curriculum learning with progressive difficulty: progressing from single commands to multi-step compound tasks, preventing the model from failing to learn effective strategies due to excessive difficulty in early stages.

One metric worth watching is cost per task. In terminal scenarios, the model often needs multiple interaction rounds to complete a task, so a low per-token price does not necessarily mean low total cost. A truly competitive model balances success rate with the number of interaction rounds. Some open-source models have already shown strong performance on this front—not by simply shrinking model size, but through post-training that enables the model to “get it right the first time,” thereby reducing wasteful interactions.

Practical Guidance for Developers

For teams integrating terminal task capabilities, consider the following engineering points:

# Pseudocode illustrating the basic terminal-agent loop
while not task_completed and step < max_steps:
    observation = env.get_state()          # Get terminal state
    action = model.plan(task, observation) # Model generates next action
    result = env.execute(action)           # Execute and capture output
    if result.error:
        context.append(parse_error(result)) # Inject error info back into context
    step += 1

The key factors are: whether error feedback is structured, whether max steps matches task complexity, and whether intermediate state is effectively compressed. These engineering details often matter more for real deployment than the model’s benchmark score.

3D Modeling: From Tool Dependency to Direct Scripting

Traditional Pipeline vs. Script-Based Generation

Traditional 3D modeling workflows rely heavily on interactive tools: modelers manually adjust vertices, materials, lighting, and cameras in a graphical interface. The advantage is intuitiveness; the disadvantages are difficulty in automation, version control, and batch generation of variations.

With large language models, a new path has emerged: generating modeling scripts directly from natural language, which in turn drive professional tools to construct models. The core value of this approach:

• Programmability: the model outputs code rather than pixels, making it naturally version-controllable and parameterizable;
• Reproducibility: the same script can be executed repeatedly in different environments with consistent results;
• Composability: multiple script snippets can be assembled into complex scenes, facilitating asset library construction.

Spatial Reasoning Is the Real Bottleneck

Getting a model to write scripts is not hard; the challenge is getting it to “understand space.” 3D modeling involves extensive spatial reasoning:

• Relative positions and orientations of objects;
• The order and results of boolean operations;
• Geometric constraints such as surface continuity, fillet radius, and wall thickness;
• Correspondence between materials and geometry.

Therefore, post-training needs to introduce a large number of modeling tasks with spatial annotations, enabling the model to build geometric intuition while learning script syntax. Evaluation systems should cover both “tool-call-only” and “pure script generation” modes—the former tests the model’s ability to use existing tools, the latter tests its understanding of geometric fundamentals.

Closed-Loop Preview and Export

Another major advantage of script-based generation is closed-loop validation. After the model generates a script, it can be executed in a sandbox environment to render a preview image; the preview is then fed back to the model for self-correction. This “generate-execute-observe-correct” loop allows the model to iterate toward a usable 3D asset without human intervention.

For developers, this enables applications of the following form:

1. The user enters a natural language description (e.g., “an engine housing with a vortex structure”);
2. The model generates a modeling script;
3. The backend executes the script and renders a preview;
4. The user adjusts parameters online (dimensions, materials, layout);
5. The result is exported to a printable format or rendered as video/motion graphics for sharing.

The critical factors in this pipeline are sandbox security and resource isolation for script execution, and preview rendering latency. The former is an engineering problem; the latter is a user experience problem. Both determine whether the product is viable.

Financial Document Reasoning: Retrieval, Cross-Referencing, and Multi-Step Numerical Reasoning

What Makes Financial Tasks Different

Document reasoning in finance differs fundamentally from general Q&A:

• Data density: a single report may contain hundreds of values with interlocking relationships;
• Cross-document dependencies: a conclusion often requires consulting multiple documents simultaneously (financial reports, announcements, research notes);
• Numerical sensitivity: an error at the second decimal place can reverse a conclusion;
• High cost of hallucination: in finance, incorrect information is far more costly than “not knowing.”

For this reason, financial tasks are typically evaluated not only on whether the final answer is correct, but also on whether the reasoning chain is traceable, citations are accurate, and numerical computations can be audited.

Capability Breakdown

A mature financial document reasoning system generally requires the following capabilities:

Capability	Description
Retrieval and localization	Locating relevant paragraphs and tables in long documents
Cross-document verification	Comparing the same metric across sources and detecting discrepancies
Multi-step numerical reasoning	Computing ratios, growth rates, and proportions from multiple values
Hallucination suppression	Explicitly stating when information is insufficient rather than fabricating answers

Common post-training approaches for improving these capabilities include constructing synthetic tasks with verifiable answers, introducing process rewards rather than only outcome rewards, and applying dedicated alignment for refusal behavior.

The Engineering Value of Hallucination Control

In financial scenarios, reducing the hallucination rate often delivers more direct value than improving accuracy. A model whose error rate drops from 20% to 8% becomes dramatically more usable in automated workflows—because human review costs are driven primarily by “detecting errors,” not “confirming correctness.”

For enterprise users, it is advisable to implement multi-level confidence thresholds during integration:

• High confidence: automatically adopt;
• Medium confidence: flag for review;
• Low confidence: refuse to answer and prompt for human intervention.

This layered strategy provides a controllable balance between efficiency and risk.

Developer Selection Guidelines

Taken together, post-training capability evolution in current Chinese models shows several clear directions:

1. From general-purpose to specialized: the same base model adapted via post-training to different vertical scenarios, rather than chasing a single leaderboard score;
2. From passive answering to active execution: models increasingly take on the role of “executors” that must interact with their environment;
3. From isolated capabilities to workflow support: evaluation is shifting from per-question accuracy to end-to-end task completion;
4. Cost and capability weighed equally: cost per task is becoming as important as accuracy as a selection metric.

For developers, it is advisable to evaluate along the following steps:

• Clarify the task type: completion, execution, or reasoning;
• Determine acceptable error rates and the proportion of work requiring human review;
• Measure cost per task, not just per-token price;
• Run A/B validation on a small set of real data, rather than relying on public benchmarks.

Conclusion

The maturation of post-training techniques is moving large models from “being able to talk” to “being able to do.” Whether in multi-step terminal execution, script-based 3D modeling, or multi-step financial reasoning, the underlying logic is the same: use high-quality data and verifiable rewards to refine general capabilities into reliable productivity.

For developers, what matters is not chasing a particular version number update, but understanding the engineering constraints and integration patterns behind these capabilities. Only by embedding model capabilities into controllable, observable, and rollback-capable system flows can the technology dividend translate into tangible business value.