Decoding Advanced Post-Training: Breakthroughs in Coding, 3D Generation, and Financial Reasoning

1. Why Post-Training is Becoming the “Productization Watershed”

As foundational model capabilities gradually converge, the post-training phase has become the critical factor determining a model’s practical value. While the industry previously focused on pre-training scale, parameter volume, and context length, engineering teams are now dedicating their efforts to high-quality supervised fine-tuning, preference alignment, tool-use training, and complex task decomposition. The goal is to shift models from “talking” to “working.”

The direct impact of this shift is significant: the same foundational model, subjected to different post-training strategies, can exhibit vastly different performance in terminal command execution, structured output, long-chain reasoning, and tool composition. In enterprise development scenarios, model stability, predictability, and low hallucination rates are often more important than benchmark scores.

The capabilities of the new generation of models discussed in this article revolve around this logic. They do not merely improve by a few points on general leaderboards; they demonstrate stronger deployability in specific domains such as coding, 3D generation, financial reasoning, and safety testing.

2. Terminal Coding Tasks: From “Writing Code” to “Operating Environments”

In the past, evaluating a model’s coding ability primarily involved checking if it could solve algorithmic problems, write functions, or complete code snippets. However, the complexity of tasks in real-world development environments goes far beyond this. Developers need models to understand the current directory structure, read logs, execute commands, fix environment dependencies, batch rename files, and even complete full troubleshooting workflows.

The value of terminal benchmarks lies in placing the model in an environment closer to actual development for evaluation. The model must not only generate executable commands but also evaluate the side effects of commands, handle error outputs, and adjust strategies based on feedback. Such tests impose higher demands on the model’s long-term planning, state tracking, and tool utilization capabilities.

According to public results, the new generation of models has entered the first tier in terminal tasks, outperforming some commercial closed-source models, especially in high-difficulty terminal tasks. More importantly, it excels in cost control. Many enterprise users consider not only absolute capability but also the cost per task. If a model can reduce the inference cost per terminal task by an order of magnitude at a comparable performance level, its advantages in batch development assistance scenarios become highly apparent.

In practical use, it is recommended that developers do not simply ask the model to “provide a command.” Instead, adopt a closed-loop approach: “task description + environment context + execution result feedback.” For example:

Find log files modified within the last three days in the current directory,
extract the request IDs containing error code E502,
sort them by time, and output the top 20.

In the first round, the model might provide a search command, but its true terminal task capability is only demonstrated when it can observe the command’s execution result and infer the next step based on that result. Therefore, when evaluating a model, prioritize testing its multi-turn tool invocation and error recovery capabilities rather than just checking if a single command is correct.

3. 3D Modeling Script Generation: A New Creative Paradigm is Emerging

3D modeling traditionally relied on graphical user interface (GUI) operations, where designers completed modeling, material adjustments, and lighting setup using a mouse and keyboard. However, with the enhancement of text-to-code capabilities, driving modeling software via scripts has become an increasingly viable approach.

The advantages of this method include reusability, version control, and batch processing. For instance, modeling an indoor forest scene traditionally might require manually placing trees one by one and adjusting materials and lighting. With scripts, a designer can describe: “Generate an indoor forest in an airport concourse, with trees distributed along both sides of the central walkway, using warm lighting.” The model then generates the corresponding modeling script, which is directly handed to the renderer for execution.

Test results show that the new generation of models, assisted by tools, can already achieve scores above 90 in professional modeling benchmarks. Compared to pure GUI operations, the efficiency improvement of script generation is highly significant. Particularly in the early design phases where frequent parameter adjustments are needed, modifying a script is more efficient than dragging and dropping a set of models.

Currently, platforms offer online 3D preview and editing capabilities, allowing users to upload or generate models, adjust them online, and export them directly for printing or sharing. This workflow holds practical value for education, product prototyping, architectural visualization, and small-batch custom manufacturing.

However, the difficulty of 3D script generation lies in spatial reasoning. The model needs to understand the relative position, size proportions, occlusion relationships, and physical stability of objects. For example, generating a 3D-printable desk lamp model requires not only ensuring an aesthetically pleasing appearance but also considering base support, center of gravity distribution, and print support structures. Therefore, in engineering practice, it is recommended to provide the model with clear spatial constraints and size ranges, and to conduct manual reviews via render previews.

4. Complex Reasoning and Hallucination Control in Financial Scenarios

Document analysis tasks in the financial sector are characterized by “long text + cross-document + numerical reasoning.” An investment report might involve dozens of pages of financial statements, footnotes, industry data, and historical comparisons. The model must first locate key information, then perform multi-step calculations, and finally output verifiable conclusions.

These tasks demand high information retrieval capabilities, numerical calculation accuracy, and citation traceability from the model. Especially in cross-document scenarios, models might confuse data from different time periods or different entities, producing seemingly reasonable but actually incorrect conclusions. Therefore, the hallucination rate in financial scenarios deserves more attention than in standard conversational scenarios.

The new generation of models has achieved leading scores on financial document question-answering benchmarks. Concurrently, internal hallucination stress tests show a significant drop in error rates compared to previous versions. This indicates that the post-training phase placed a strong emphasis on optimizing factual consistency and numerical reasoning.

In practical deployment, it is recommended to combine Search Augmented Generation (RAG) mechanisms to map the model’s output to original text fragments. Although the model’s inherent capabilities have improved, manual verification should still be retained for critical financial data. For example, the model can output the following structure:

Conclusion: Company A's free cash flow for Q3 2025 is approximately 1.23 billion.
Basis: See Cash Flow Statement on page 18 and Note 7 on page 22 of the "2025 Q3 Financial Report".
Calculation Process: Operating cash flow 1.51 billion - Capital expenditure 0.28 billion.

This “conclusion + basis + process” output format helps mitigate hallucination risks and facilitates subsequent auditing.

5. Cybersecurity Capabilities Require Continuous Boundary Testing

As model capabilities improve, their potential impact in the cybersecurity domain has garnered increased attention. On one hand, models can assist security teams with log analysis, vulnerability clue consolidation, and penetration test script generation. On the other hand, without effective constraints, models could be abused to generate attack payloads or bypass protection strategies.

Therefore, model providers typically introduce safety alignment mechanisms to identify and intercept sensitive requests during both training and inference. According to public information, the new generation of models has reinforced safety boundaries alongside the expansion of post-training scale. However, such capabilities are difficult to measure through a single metric; they require continuous evaluation combining red teaming, adversarial samples, and real-world business scenarios.

When enterprises integrate models, it is recommended to add a secondary risk control strategy at their application layer. This includes performing sensitive pattern matching on generated content, restricting specific tool invocation permissions, and applying manual or rule-based reviews to all execution-oriented outputs.

6. Balancing Cost, Performance, and Deployability

Technical selection is never just about achieving the highest score. For developers and enterprise users, whether a model is suitable for deployment requires considering at least four dimensions: task completion quality, unit cost, response latency, and ecosystem compatibility.

The new generation of models demonstrates an excellent performance-to-cost ratio in terminal and 3D generation tasks. This means that in daily, high-frequency development assistance scenarios, users can achieve results close to top-tier models at a lower cost. For budget-constrained teams, such models are better suited as default work engines, reserving more expensive models for a minority of high-difficulty tasks.

From an engineering practice perspective, adopting a tiered routing strategy is recommended:

• Routine code completion, document organization, and script generation: Use cost-effective models.
• Complex architecture design and cross-module troubleshooting: Invoke models with stronger reasoning capabilities.
• Final code review and security-sensitive tasks: Add manual reviews or rule engines.

This tiered approach ensures quality while controlling overall costs.

7. Conclusion and Recommendations

The capability enhancements of the new generation of models in the post-training phase are no longer confined to language understanding; they extend toward tool operation, professional modeling, and complex reasoning. For developers, model evaluation should start from real-world tasks, focusing on multi-turn tool invocation, error recovery, factual consistency, and unit cost, rather than relying solely on static leaderboards.

It is recommended that enterprise users prioritize pilot programs in the following scenarios:

1. Terminal Environment Troubleshooting: Integrate the model into the development environment for log analysis, batch operations, and dependency repair.
2. Rapid 3D Prototyping: Replace manual modeling work with script-driven approaches.
3. Automated Financial Report Summarization: Combined with source text verification, improve information extraction and preliminary analysis efficiency.
4. Security Log Analysis: Test the model’s anomaly pattern recognition capabilities in a controlled environment.

The improvement of model capabilities brings new space for application innovation, but the effectiveness of deployment still depends on engineering capabilities, quality evaluation systems, and safety boundary design. Only by embedding model capabilities into real workflows and establishing continuous evaluation and feedback mechanisms can their true value be unlocked.