LLM Post-Training Capabilities: A Five-Dimensional Evaluation and Implementation Guide

Introduction: Post-Training Dictates Productivity, and Evaluation Must Catch Up

Over the past two years, the competitive landscape of large language models (LLMs) has experienced a significant paradigm shift. Pre-training addresses whether a model “possesses the knowledge,” whereas post-training—including Supervised Fine-Tuning (SFT), Reinforcement Learning from Human Feedback (RLHF), and Reinforcement Learning with Verifiable Rewards (RLVR)—determines whether it “uses that knowledge effectively.” Multiple public industry practices point to the same conclusion: the same base model, after undergoing high-quality post-training iterations, can see its performance in practical tasks such as coding, tool usage, and professional reasoning surge significantly, sometimes exceeding the gains of a medium-scale pre-training upgrade.

This poses new challenges for evaluation systems. The traditional “leaderboard-chasing” mindset—comparing aggregate scores on a single static benchmark—can no longer answer the questions enterprises truly care about: Once the model is integrated into an actual business workflow, can it deliver consistently? What is the cost of failure? Is the overall cost controllable?

A more practical approach is to decompose model capabilities into several dimensions directly corresponding to production scenarios, design specific evaluations for each, and then synthesize the trade-offs. Based on current industry trends, the following five dimensions deserve the most attention:

• Code and terminal agent capabilities
• 3D modeling and spatial reasoning
• Financial and professional document analysis
• Hallucination control
• Cybersecurity capabilities

The following sections will explore each dimension in detail and conclude with a deployable blueprint for building an evaluation pipeline.

Dimension 1: Code Capabilities — From “Function Completion” to “Terminal Operation”

Early coding benchmarks mostly tested “completing an implementation given a function signature.” In contrast, the new generation of terminal-based benchmarks tests comprehensive Agent capabilities: the model is placed in a real command-line environment and must autonomously perform a series of operations—reading the codebase, installing dependencies, running tests, and self-correcting based on error messages—until the task objective is met. The core evaluation points for these benchmarks include:

1. Multi-turn tool call stability: High single-step generation quality does not guarantee an error-free chain of dozens of steps.
2. Environmental feedback utilization: Can the model accurately interpret error messages, locate root causes, and retroactively correct them?
3. Long-range planning: Can it break down vague objectives into an executable sequence of steps?
4. Operational safety: It must not破坏 the environment or make unintentional destructive changes.

According to public evaluation data, the completion rate of even the most advanced frontier models on terminal-based benchmarks hovers around 65%, leaving a clear gap before “reliable delivery” is achieved. This high discriminative power makes it an essential test item for model selection.

Another easily overlooked metric is cost per unit capability. Some low-cost models are indeed cheap per call, but if their task completion rate is low, the comprehensive cost after repeated retries actually becomes higher. When evaluating, it is recommended to introduce the following estimation formula:

# Formula: Expected total cost
Expected_Total_Cost = Single_Call_Cost * Average_Interaction_Rounds / Task_Completion_Rate

Only by placing “can it be done” and “how much does it cost to do it reliably” into the same formula will the selection conclusion be convincing.

Dimension 2: 3D Modeling and Spatial Reasoning — Enabling Models to “Act Directly”

The technical path of 3D generation is undergoing a crucial evolution:

• Phase 1: The model outputs natural language descriptions, which humans manually implement in modeling software.
• Phase 2: The model indirectly interacts with modeling software via external tool invocation protocols.
• Current Phase: The model directly writes scripts for professional modeling tools, programmatically driving the software’s native capabilities to build assets and refine details, adjusting layouts, materials, and geometry programmatically.

The significance of the third phase lies in granting the model end-to-end control over the modeling process by removing the middle layer. The scripts are readable, editable, and version-controllable. The geometric results are precise and controllable, and can be directly fed into downstream rendering pipelines or 3D printing workflows.

When evaluating this dimension, four aspects require attention:

1. Geometric correctness: Are the meshes manifold, normals consistent, and topology clean?
2. Visual quality: The reasonableness of materials, lighting, and composition.
3. Manufacturability: For 3D printing scenarios, process constraints like wall thickness and overhang angles must be validated.
4. Iterative capability: Can it continue refining an existing scene rather than starting from scratch every time?

Public benchmarks indicate that top models already achieve scores over 90 on tool-dependent CAD tasks, approaching the ~96-point level of the strongest closed-source models. This indicates that “script-driven modeling” has transitioned from a technical demo into practical utility. Supported by product-side features like online previewing, editing, and exporting model files or demo videos, the workflow of “modeling from a single prompt and sharing instantly” is now viable.

Below is a sample code snippet illustrating the basic paradigm of “model outputs script -> modeling engine executes”:

# Sample: Parametric modeling script generated by an LLM (pseudo-code)
def build_desk_lamp():
    scene = Scene.new(unit="mm")
    base = scene.add_cylinder(radius=60, height=8)
    base.set_material("matte_plastic", color="#2b2b2b")
    arm = scene.add_curve_extrude(
        path=[(0, 8, 0), (0, 90, 0), (35, 130, 0)],
        thickness=6,
    )
    arm.set_material("anodized_aluminum")
    shade = scene.add_cone(radius_top=18, radius_bottom=45, height=50)
    shade.translate((35, 155, 0))
    shade.set_material("diffuser", transmittance=0.82)
    scene.check_printability(min_wall_mm=1.2)   # Process constraint validation
    return scene.export("desk_lamp.stl")

Dimension 3: Financial Document Analysis — A Composite Test of Retrieval, Verification, and Multi-Step Reasoning

The financial sector demands the highest level of model rigor. Real-world financial documents often exhibit the following characteristics: varying scan quality, deeply nested tables, numerous footnotes and caliber explanations, and critical data scattered across different pages or even different documents. Therefore, a robust financial document benchmark should simultaneously test three layers of capability:

1. Retrieval and location: Finding relevant data points within hundreds of pages of material.
2. Cross-document verification: Performing consistency checks on data from different sources and identifying caliber discrepancies.
3. Multi-step numerical reasoning: Calculating ratios, verifying articulation relationships, and outputting a reviewable calculation process.

Public industry evaluations show that leading models generally score just over 60 on such composite benchmarks, far from saturation. For teams planning to integrate models into financial analysis or investment research workflows, it is recommended to build a custom evaluation set “desensitized from real business data” and adhere to three principles: cover both high-frequency and long-tail questions, ensure answers are machine-judgable, and strictly isolate evaluation samples from any training data.

Dimension 4: Hallucination Control — The Hidden Threshold for Reliability

The stronger the model, the more insidious and harmful its hallucinations become. A confidently stated incorrect number can directly translate into a decision-making disaster in financial, legal, or medical contexts. Therefore, the hallucination rate should not be viewed as an “add-on metric” but as a veto-capable threshold requirement.

Key design elements for a hallucination evaluation set include:

• Adversarial questioning: Embedding false premises into questions to see if the model will fabricate answers following the false premise.
• Unanswerable questions: When an answer does not exist in the document, the model should refuse to answer rather than confabulate.
• Fine-grained fact-checking: Verifying atomic-level facts such as names, numbers, dates, and units one by one.

Post-training iterations have a significant impact on hallucination governance. Public data shows that through a single round of systematic iteration, some frontier models have reduced error rates in internal stress tests from over 20% in the previous generation to under 10%. However, the engineering side should not operate unprotected. It is recommended to stack four lines of defense:

1. Adopt Retrieval-Augmented Generation (RAG) and enforce citation tracing for critical conclusions.
2. Trigger clarification or refusal for low-confidence outputs.
3. Use a rule engine or an independent verification model for factual consistency post-processing.
4. Isolate evaluation data from training data to prevent score inflation caused by “memorizing the test.”

Dimension 5: Cybersecurity Capabilities — A Sharp Blade That Needs Guardrails

An important industry observation is that as post-training scales expand, a model’s vulnerability research capabilities continue to improve—including reading disassembled code, analyzing patch diffs, and understanding vulnerability trigger conditions. This entails both an advantage and a risk for enterprises:

• Benefit: Security teams can use them as automated audit assistants to accelerate code auditing, patch analysis, and attack surface mapping.
• Risk: The stronger the capability, the stricter the authorization boundaries required.

Compliance baselines during implementation should include: using these capabilities only in authorized test environments; filtering sensitive content from model outputs; retaining comprehensive audit logs; and clearly defining responsibility ownership within the team. The evaluation of security capabilities itself should also be conducted within a sandboxed cyber range environment to ensure the evaluation process itself does not become a source of risk.

Build Your Own Evaluation Pipeline

With the dimensional framework in place, the final step is to solidify it into a repeatable engineering process:

1. Dataset construction: Sample and desensitize from real business operations, stratify by business importance, and ensure coverage of high-frequency and long-tail scenarios.
2. Infrastructure: Use a container platform to provide isolated execution sandboxes (essential for terminal tasks), object storage to manage evaluation samples and artifacts, and logging services to retain step-by-step processes for playback and root-cause analysis. General capabilities like the container platform, object storage, and logging services provided by cloud providers such as Baidu AI Cloud can be quickly combined to assemble such a pipeline.
3. Automated execution: Unify the evaluation entry point and mask differences between model APIs. An illustration is as follows:

# Sample: Core evaluation scheduling logic (pseudo-code)
for task in dataset:
    sandbox = pool.acquire(image=task.env_image)    # Isolated environment
    result = model.run(task.prompt, tools=sandbox.toolset)
    score = judge(task, result, rubric=task.rubric)  # Prioritize machine-judgable metrics
    metrics.add(task.tag, score)
    pool.release(sandbox)
report = metrics.summarize(ci=0.95)   # Output confidence interval rather than a single-point score

1. Result interpretation: Run each task at least 3 times to obtain the mean and observe variance. Perform significance testing when comparing models with adjacent scores to avoid being misled by noise.

Conclusion

Model iteration in the post-training era has transformed “capability evaluation” from a one-off selection step into an engineering system requiring continuous operation. Code agents rely on stability and unit cost; 3D modeling depends on programmatic control; financial analysis demands composite reasoning; hallucination rate acts as a threshold; and security capabilities require guardrails. Together, these five dimensions form a model profile that closely mirrors real-world production. For enterprise teams, solidifying real business samples into a private evaluation set early on answers that ultimate question far better than chasing any public leaderboard: Is this model truly worth using in my specific scenario?