The Evolution of LLM Post-Training: From Code Generation to 3D Modeling

1. The Post-Training Paradigm: A Key Path for Capability Leaps

Current improvements in Large Language Models (LLMs) no longer rely solely on parameter scaling during the pre-training phase. As foundational model architectures mature, post-training has become the critical factor determining a model’s actual performance. Post-training refers to the process of enhancing capabilities for specific task distributions through supervised fine-tuning, reinforcement learning, and preference alignment after completing large-scale pre-training.

The value of this paradigm lies in its division of labor: the foundational model provides general language understanding and generation capabilities, while post-training defines the “usability ceiling” in specific scenarios. For high-difficulty tasks such as code generation, spatial reasoning, and tool invocation, the quality of post-training often differentiates models more significantly than the scale of pre-training.

Recent iterations of Chinese-developed LLMs have visibly accelerated their post-training efforts. Taking a recent version update as an example, targeted post-training based on the previous foundational model achieved significant enhancements across multiple dimensions, including coding and 3D modeling. This combination strategy of “foundational model + deep post-training” is becoming the mainstream technical route in the industry.

2. Coding Capabilities: Terminal-Level Task Evaluation and Cost Optimization

2.1 The Significance of Terminal-Level Code Tasks

Traditional code evaluations mostly focus on function-level completion or algorithmic problem-solving, but these tasks have a noticeable gap compared to real-world development scenarios. Developers routinely face multi-step tasks in terminal environments: executing commands in the command line, reading files, debugging errors, and iterating modifications. These tasks demand higher comprehensive capabilities from the model, involving:

• Environmental awareness: Understanding the current working directory, dependency status, and system constraints.
• Multi-turn decision-making: Dynamically adjusting strategies based on previous execution results.
• Tool invocation: Correctly using command-line tools for file operations, builds, and testing.
• Error recovery: Identifying failure causes and autonomously correcting them.

Therefore, evaluating code tasks oriented toward terminal environments better reflects a model’s usability in actual engineering scenarios.

2.2 Balancing Performance and Cost

In terminal-level code tasks, the performance gap among frontier models is narrowing, but cost differences have become a key consideration for enterprise selection. Even if a model leads in scores, if the cost per task invocation is too high, it remains difficult to deploy in large-scale automation scenarios.

According to public evaluation data, deeply post-trained Chinese models have entered the first tier in terminal-level code tasks, with some metrics approaching international frontier levels. More noteworthy is cost control: for equivalent tasks, the invocation costs of some Chinese models are significantly lower than other solutions in the same performance range, providing a more cost-effective choice for scenarios requiring high-frequency code agent invocations.

Evaluation Dimension	Focus	Engineering Significance
Task Completion Rate	End-to-end success rate of multi-step tasks	Determines automation flow reliability
Cost per Task	Overall overhead per invocation	Affects large-scale deployment feasibility
Error Recovery Rate	Proportion of autonomous corrections after failure	Reduces manual intervention frequency
Response Latency	End-to-end time consumption	Impacts interactive experience

2.3 Engineering Practice Recommendations

For teams looking to integrate code agents into their R&D workflows, evaluation is recommended from the following perspectives:

1. Task Tiering: Layer code tasks by complexity; use low-cost models for simple tasks and high-capability models for complex ones.
2. Cache Reuse: Cache generation results for highly repetitive code snippets to reduce redundant invocations.
3. Human-Machine Collaboration: Retain manual confirmation at critical nodes to avoid the risks of full automation.
4. Continuous Evaluation: Build an internal evaluation set to regularly validate model performance in your specific business scenarios.

3. 3D Modeling: From Tool Dependency to Direct Script Generation

3.1 Spatial Reasoning Challenges in 3D Creation

The capability requirements for 3D modeling tasks fundamentally differ from plain text generation. It requires the model to possess:

• Spatial reasoning: Understanding position, size, and orientation relationships in 3D space.
• Geometric understanding: Mastering geometric concepts like meshes, surfaces, and boolean operations.
• Tool manipulation: Driving professional modeling software to perform actual modeling actions.
• Layout planning: Coordinating the positions and relationships of multiple objects at the scene level.

Traditional solutions typically rely on middleware like MCP (Model Context Protocol) to connect the model and modeling tools, where the middleware translates model outputs into tool instructions. While feasible, this approach increases system complexity and invocation overhead.

3.2 The Technical Path of Direct Script Generation

An important direction in post-training is enabling the model to directly generate script code for professional modeling tools, bypassing the middleware to drive tool execution directly. Taking a common 3D modeling tool as an example, its scripting API typically supports:

# Illustrative code: Creating a basic geometric object and setting material via script
import modeling_api
# Create a basic geometric object
obj = modeling_api.create_primitive(
    type="cube",
    location=(0, 0, 0),
    scale=(1, 1, 1)
)
# Set material properties
material = modeling_api.create_material(
    name="surface_mat",
    base_color=(0.8, 0.8, 0.8, 1.0),
    roughness=0.5
)
modeling_api.assign_material(obj, material)
# Adjust layout
modeling_api.translate(obj, offset=(2, 0, 0))

The model needs to understand the semantics of these scripting APIs and generate the correct script sequence based on natural language descriptions. This requires introducing a large amount of script-description paired data during the post-training phase and optimizing the correctness of the generated results through reinforcement learning.

3.3 Evaluation Performance and Capability Boundaries

In evaluations oriented toward computer-aided design, post-trained models have achieved high scores under tool-dependent conditions, narrowing the gap with frontier closed-source models to single digits. This means that on the path of script-driven modeling, these models already possess practical-level capabilities.

Specific capabilities include:

• Asset construction: Creating basic geometric objects from scratch and combining them into complex structures.
• Detail refinement: Performing operations like beveling, subdividing, and smoothing on existing models.
• Layout adjustment: Coordinating the positional relationships of multiple objects in a scene.
• Material editing: Setting material parameters such as color, roughness, and metallic properties.
• Geometric modification: Executing geometric operations like boolean operations, extrusion, and beveling.

3.4 Impact on the Creation Workflow

The maturation of direct script generation capabilities can impact the 3D creation workflow in two ways:

First, it lowers the barrier to tool usage. Creators do not need to master the complex interfaces of modeling software; they can generate scripts via natural language descriptions to quickly validate ideas. This is particularly valuable for iterations during the conceptual design phase.

Second, it improves batch processing efficiency. For scenarios requiring the generation of numerous similar variants (such as game assets or architectural components), script-based generation can significantly reduce repetitive labor.

However, it is also necessary to recognize current capability boundaries: scripts generated by the model may still require manual debugging, and professional operations like topology optimization for complex scenes and UV unwrapping still rely on experienced modelers.

4. Technology Trends and Selection Considerations

4.1 Differentiated Competition in Post-Training Capabilities

Judging from recent progress, post-training phases of Chinese models exhibit a trend of differentiated competition: some models focus on coding and tool invocation capabilities, while others emphasize multimodal and spatial reasoning. This differentiation raises higher requirements for enterprise selection—businesses need to match model capabilities to their specific operational scenarios.

4.2 Layering of General and Proprietary Capabilities

A notable trend is that foundational models provide a general capability base, while post-training deeply reinforces specific domains. When building AI applications, enterprises can consider:

• General Layer: Using foundational models for daily text understanding and generation.
• Professional Layer: Using post-trained specialized models for high-difficulty tasks like coding and modeling.
• Orchestration Layer: Coordinating multiple models through workflow engines to achieve complex task decomposition.

4.3 Dynamic Balance of Cost and Performance

As model capabilities improve, cost structures are also changing. During model selection, instead of looking solely at the single invocation price, enterprises should comprehensively evaluate:

• Implicit cost savings brought by task completion rates.
• Reduced manual intervention costs due to error recovery capabilities.
• The impact of response latency on user experience.
• The long-term value brought by model iteration speed.

5. Conclusion

The maturation of post-training technology is shifting LLMs from being “conversational” to being “capable of executing work.” The improved performance of code agents in terminal-level tasks, alongside breakthroughs in direct script generation for 3D modeling, both point in the same direction: models are becoming execution engines capable of directly driving professional tools.

For developers and enterprises, understanding the technical logic of post-training, evaluating model performance in specific scenarios, and establishing a reasonable strategy to balance cost and performance will be critical capabilities for AI application deployment. As Chinese models continue to invest in post-training, the technological evolution in this field warrants long-term attention.