Post-Training Capability Leap in Domestic Models: Engineering Practice in Code, 3D Scripts, and Professional Reasoning

Over the past year, competition among large language models has gradually shifted from “pre-training scale” to “post-training quality.” After a base model is released, what truly determines its usability in developers’ hands is often the subsequent instruction fine-tuning, reinforcement learning, and domain data mixing. This article uses a recent post-training version update of a domestic model as an entry point to discuss the evaluation logic and engineering implementation approaches across four directions: code agents, 3D script generation, financial document reasoning, and safety capabilities—and offers reusable practical recommendations.

It should be noted that this article does not recommend or benchmark any specific commercial product. The data referenced comes from abstract discussions of publicly available evaluation methodologies. The focus is on helping readers build an analytical framework for “how to view this kind of capability upgrade.”

1. Why Post-Training Versions Deserve Separate Attention

The base model determines the capability ceiling; post-training determines capability “usability.” The same base model under different post-training strategies can perform a full tier apart, for three main reasons:

1. Task distribution alignment: The agent trajectories, tool-calling samples, and domain documents injected during post-training directly determine the model’s stability in real workflows.
2. Reasoning chain length control: Excessively pursuing long-chain reasoning drives up latency and cost. Post-training allows trade-offs at a “just enough” reasoning depth.
3. Format and tool contracts: Code tasks and script generation tasks are extremely sensitive to output format. Post-training can significantly reduce the probability of “correct format but wrong content.”

Therefore, evaluating a version update requires more than looking at absolute scores on a single leaderboard—it should focus on the “score–cost–stability” triad.

2. Code Agents: Terminal Tasks Are the Real Test

In code capability evaluation, unit-test-style benchmarks (such as function-level completion) have gradually saturated. What truly provides differentiation is multi-step tasks in terminal environments: given a real repository and a vague requirement, the model must complete a series of actions through the command line—environment exploration, dependency installation, file modification, and test verification.

These evaluations typically focus on three metrics:

• Task pass rate: End-to-end completion ratio;
• Per-task cost: Token consumption and number of tool calls;
• Failure recovery rate: Whether the model can self-correct after an initial execution failure.

From public discussions, current domestic models can already reach the first tier on terminal-type tasks, with relatively better cost control. This is significant for engineering teams: when performance is close, cost and latency are often the deciding factors for selection. A solution with a slightly lower pass rate but half the per-task cost is usually more economical in high-frequency scenarios such as CI pipelines and batch refactoring.

Engineering recommendations:

# Design approach for a minimal validation set when evaluating terminal agents
# 1. Select issues from 20 real repositories
# 2. Fix container images and dependency versions to avoid environment drift
# 3. Record pass rate, average tool calls, average time
# 4. Manually categorize failure samples: environment issues / comprehension errors / format errors

Only by categorizing failure samples can you determine whether it’s “insufficient model capability” or “unreasonable scaffolding design.”

3. 3D Script Generation: A New Path That Bypasses Tool Protocol Dependencies

The mainstream approach in 3D modeling has been to have models call MCP (Model Context Protocol)-style interfaces of modeling software, with external services performing geometric operations. The problems with this approach are: high toolchain coupling, poor portability, and difficult debugging.

The new approach is to have the model directly generate modeling scripts (e.g., generating script code for geometry construction, material assignment, and layout adjustment based on scene descriptions), which are then executed by the host software. The advantages are clear:

• Auditable: The script is the artifact; errors can be located by reading the code directly;
• Versionable: Scripts can be committed to Git, turning the modeling process into reproducible engineering assets;
• Composable: Script fragments can be reused, forming an internal modeling component library within a team.

From public evaluations, on CAD-type benchmarks that rely on tool calls, top models can already approach the 90-point range, and open-source solutions can reach around 92 points. This means script-based modeling has crossed the threshold from “demonstrably feasible” to “production-ready.”

A typical workflow can be abstracted as:

# Pseudocode: General pipeline for script-based modeling
def build_scene(description):
    geometry = generate_geometry_script(description)   # Geometry construction
    materials = assign_materials(geometry, description) # Materials
    layout = adjust_layout(geometry, description)       # Layout
    return compose(geometry, materials, layout)

From an engineering perspective, note that script generation itself also requires a sandbox. It is recommended to execute generated scripts inside containers, restrict file system and network access, and set execution time limits to avoid infinite loops or resource exhaustion.

Additionally, online preview and editing capabilities are becoming essential companion features. After the model generates a script, users need to see results immediately, make local adjustments, and then export to a printable format or shareable asset. This “generate–preview–edit–export” loop determines the actual user experience more than point-generation capability alone.

4. Financial Document Reasoning: Retrieval, Verification, and Multi-Step Numerical Computation

The difficulty in financial scenarios is not “reading documents” but cross-document verification and multi-step numerical reasoning. A financial report analysis may involve: retrieving metrics from multiple source documents, reconciling differences in accounting standards, performing year-over-year and quarter-over-quarter calculations, and outputting conclusions. These tasks are typically designed as multi-hop question-answering evaluations, testing the model’s retrieval precision and numerical stability.

From public discussions, current top models can already achieve scores above 60 on such benchmarks and approach the first tier in professional investment workflow evaluations. However, there are three pitfalls in engineering implementation:

1. Numerical hallucination: The model may “calculate wrong but sound plausible.” Executable calculation steps must be used for secondary verification;
2. Standard drift: Different documents use different statistical standards. These must be explicitly constrained in prompts;
3. Citation traceability: Every conclusion should be bound to its source location for manual review.

Hallucination rate is the core metric in this scenario. Public information shows that some models, through post-training, have reduced error rates from above 20% to within 10% in internal stress tests. For financial applications, every one-percentage-point drop in error rate linearly reduces manual review costs—this has more business value than a pure score improvement.

5. Safety Capabilities: The Double-Edged Sword of Scaling Post-Training

A concerning phenomenon: as post-training scale expands, models improve on safety tasks such as vulnerability identification and code auditing, but simultaneously the risk of misuse also rises. Therefore, safety evaluation typically includes two dimensions:

• Defensive capability: Whether the model can discover injection, out-of-bounds, and permission flaws in code;
• Refusal boundary: Whether it can stably refuse clearly malicious requests.

When integrating such capabilities, engineering teams should establish a “capability–permission” matching mechanism: high-risk operations (such as generating executable attack scripts or batch-modifying production configurations) must have additional human confirmation steps and cannot rely solely on the model’s own refusal strategy.

6. Recommendations for Selection and Deployment

Synthesizing the four directions above, here is an actionable assessment framework:

1. Define the scenario before choosing the model: For terminal code tasks, look at pass rate and per-task cost; for 3D, look at script executability and sandbox stability; for finance, look at hallucination rate and citation traceability.
2. Build your own small evaluation set: Public leaderboards can only serve as initial screening. 20–50 real business samples better reflect actual performance.
3. Account for cost by “task” rather than “token”: Include tool calls, retries, and manual review in the per-task cost.
4. Scaffolding matters more than the model: Sandboxes, retry strategies, failure categorization, and result verification—these engineering investments often yield greater returns than switching models.
5. Front-load safety boundaries: High-risk capabilities should be disabled by default and opened based on role authorization.

Conclusion

The pace of post-training version iterations is accelerating, and discussions about “version X surpassing competitor Y” will only increase. For developers, what is more valuable than chasing single leaderboard rankings is building an evaluation methodology centered on real tasks that accounts for both cost and safety. Code agents, script-based 3D modeling, financial document reasoning, and safety auditing happen to cover the four most deployment-valuable scenarios today. Once evaluation sets, sandboxes, and verification pipelines are in place, any model upgrade can be quickly converted into productivity—rather than remaining in news headlines.