Why Post-Training Has Become the Key to Capability Leaps

LLM development has shifted from “stacking parameters and competing on pretraining” toward “refined post-training and stronger alignment.” Once a base model is released, supervised fine-tuning, preference optimization, tool-call reinforcement, and domain data retraining often deliver performance far beyond the original version on specific tasks. The value of this “post-training enhancement” path is clear: it achieves significant improvements in high-value scenarios such as code, spatial reasoning, and document understanding at a relatively low compute cost.

Recent industry trends show leading model teams advancing along two main tracks. The first is terminal-level code tasks, where models complete multi-step operations, file management, and script orchestration in real command-line environments. The second is structured generation tasks, where models directly produce modeling scripts or engineering files that professional software can parse. The common thread: models are no longer just “chatting”—they are becoming executable, verifiable productivity components.

Code Capabilities: From Completion to Terminal-Level Task Execution

How Evaluation Criteria Have Shifted

Traditional code benchmarks focus on function-level completion or algorithm puzzles, while new-generation benchmarks emphasize end-to-end task completion. Terminal-style benchmarks, for example, require a model to understand instructions, plan steps, execute commands, handle errors, and deliver results in a simulated environment. These tasks are closer to real development work and better reflect engineering usability.

On this dimension, several models from Chinese teams have entered the top tier, with the gap to leading international counterparts narrowing noticeably. In the “performance–cost” trade-off in particular, keeping task pass rates high while lowering inference overhead has become a core criterion for enterprise selection.

Why Cost Control Matters

For enterprises calling models at scale, per-inference cost directly determines business viability. A model that performs close to top-tier levels on terminal tasks but costs significantly less per call often delivers more value in high-frequency scenarios. Automated testing, batch script generation, and CI/CD assistance are far more sensitive to cost than to single-shot peak performance.

Practical recommendations:

• Tier tasks by complexity: route simple tasks to lower-cost models and escalate complex ones;
• Build a combined score of “pass rate × per-call cost” instead of relying on a single metric;
• Add retries and result validation for terminal tasks to reduce dependence on any single successful run.

3D Modeling: Script Generation That Completes the Creation Pipeline

From Conversation to Scripts

3D modeling has long depended on manually operating professional software, with a steep learning curve. The breakthrough of new-generation models is writing modeling scripts directly, driving professional tools through geometry construction, material adjustment, and layout refinement. Users no longer need to master complex interfaces—describing requirements in natural language is enough for the model to generate executable scripts.

Compared with approaches that rely on intermediate protocols or plugins, script-based generation has a shorter pipeline and stronger controllability. Developers can place generated scripts under version control, enabling reproducible, iterative modeling workflows.

Spatial Reasoning and Structured Output

3D modeling places high demands on spatial reasoning: understanding coordinate systems, proportions, geometric constraints, and hierarchical structure. In relevant benchmarks, leading models already handle fairly complex modeling tasks, scoring close to top-tier levels especially in scenarios that depend on tool calling.

Typical applications include:

• Scene building: quick prototypes such as indoor landscapes and building structures;
• Mechanical parts: rotating structures and assembly modeling;
• Product design: printable models such as lamps and enclosures.

Illustrative script structure (pseudocode):

# Create the base geometry
base = create_cube(size=(2, 2, 0.5))
# Apply a material
apply_material(base, "matte_white")
# Duplicate in an array
for i in range(5):
    clone = duplicate(base, offset=(i*2.2, 0, 0))
# Export to a printable format
export_stl("output.stl")

Online Preview and the Sharing Loop

Some platforms have integrated modeling capabilities into online environments, supporting preview, editing, export, and sharing. This closed loop lowers the barrier from “idea” to “physical object”: users can model in a browser, then export directly for 3D printing, or generate videos and animated images for distribution. For creators, this means integrated content production and distribution.

Finance and Document Analysis: Putting Multi-Step Reasoning to Work

Financial document analysis is a touchstone for overall model capability: it requires retrieval, cross-document verification, multi-step numerical reasoning, and conclusion generation. Relevant benchmarks typically simulate real financial reports, investment workflows, and compliance reviews, testing a model’s stability over long contexts and complex logic.

Breaking Down the Key Capabilities

• Retrieval and locating: finding key clauses and data across large document sets;
• Cross-document verification: checking data consistency across different sources;
• Multi-step numerical reasoning: computing ratios, trends, and valuations;
• Conclusion generation: producing structured analysis results.

In this field, leading models already cover the full workflow from target screening to profitability analysis. For financial institutions, this means repetitive analysis tasks can be automated, freeing professionals to focus on judgment and decision-making.

Hallucination Control

Financial scenarios demand high accuracy, and hallucination is unacceptable. New-generation models have significantly reduced error rates in internal stress tests, with some approaching best-in-class levels. Practical recommendations:

• Add secondary validation and external tool-based computation for critical numbers;
• Preserve reasoning traces for auditing and traceability;
• Insert human review checkpoints for high-risk conclusions.

Content Safety and Compliance

As model capabilities grow, content safety and compliance become prerequisites for deployment. Models must identify sensitive content during generation, follow policy constraints, and refuse inappropriate requests when necessary. This is not only a technical issue but also a requirement for productization and commercialization.

Approach:

• Introduce multi-layer filtering during both training and inference;
• Build configurable safety policies to fit different business scenarios;
• Continuously monitor and sample outputs to form a feedback loop.

Selection and Deployment Recommendations

Facing rapidly iterating model capabilities, enterprises and developers can evaluate along these dimensions:

1. Task fit: whether core scenarios such as code, modeling, and document analysis are covered;
2. Cost structure: per-call cost and volume discounts;
3. Controllability: script-based output, reproducibility, and auditability;
4. Safety and compliance: alignment with industry regulation and internal policy;
5. Ecosystem integration: ease of connecting to existing toolchains and workflows.

Recommended path:

• Start with a small-scale pilot to validate results;
• Then route tasks in tiers to balance performance and cost;
• Finally, establish a continuous evaluation mechanism and update strategy as models iterate.

Conclusion

Refined post-training is moving large models from “general conversation” toward “professional execution.” Capability leaps in code, 3D modeling, financial analysis, and content safety signal that models are becoming infrastructure embedded in production workflows. For developers, understanding the boundaries of these capabilities and how to integrate them is more valuable than chasing a single benchmark score. As script-based generation and tool calling mature further, model-driven automated workflows will become the norm.