I. The Post-Training Paradigm: Engineering Logic for Capability Jumps

As the iteration pace of large models accelerates, a key trend has emerged for developers: while the capability ceiling of foundation models is crucial, the post-training phase ultimately dictates the real-world deployment experience. Systematically post-training previous-generation foundation models to achieve significant enhancements in verticals—such as code intelligence, 3D content generation, and financial document reasoning—while keeping inference costs within a reasonable range, is becoming the industry’s mainstream technical choice.

The value of post-training lies in “directional gain.” While pre-training pursues broad-spectrum knowledge and general language abilities, post-training shifts the model’s behavior distribution toward specific tasks using Supervised Fine-Tuning (SFT), preference alignment, tool-calling trajectory injection, and Verifiable Reinforcement Learning (RL). This shift is not merely about feeding more data; it requires synergistic optimization across three dimensions: data construction, reward signal design, and inference budget allocation.

From an engineering perspective, post-training presents three core challenges:

• Data construction density: High-quality trajectories for vertical tasks are scarce. They must be expanded through programmatic synthesis, environment replay, and expert trajectory distillation.
• Reward signal verifiability: Code tasks can be evaluated via unit tests, and 3D scripts via rendering results and geometric validation. However, open-domain reasoning tasks require more granular process rewards.
• Cost-performance Pareto frontier: Simply piling on inference compute can boost scores, but engineering truly requires “effective capability per unit cost.”

Let’s explore these concepts along three main technical tracks.

II. Code Agents: Balancing Cost and Performance on Terminal Tasks

Code tasks are the ultimate proving ground for testing a model’s “agent quality.” Unlike single-turn code completion, terminal tasks require the model to perform multi-step operations within a real command-line environment: reading files, modifying source code, running builds, parsing errors, and iteratively fixing them. These tasks evaluate long-horizon planning, tool-calling accuracy, and error recovery capabilities.

In evaluating terminal-based code tasks, a notable trend is that open-source models have entered the “near-top-tier” bracket for the first time. For instance, a post-trained model achieved a score of 65.7 on a terminal task benchmark, second only to leading closed-source models and surpassing several contemporary international mainstream models. The technical implications of this result are more significant than the score itself.

First, the quality of tool-calling trajectories dictates the upper limit. The success rate of terminal tasks heavily depends on the model’s understanding of shell semantics—knowing the relationship between cd and relative paths, knowing which part of the log to check first when a build fails, and knowing when to roll back rather than apply another patch. Injecting a large volume of real terminal session trajectories (both successful and failed samples) during post-training significantly improves the model’s recovery behavior on long-tail errors.

Second, inference budget allocation strategies impact cost. A common misconception is equating “high scores” with “multiple sampling for optimal selection.” A more feasible engineering approach is a tiered budget: simple tasks use short-chain reasoning, while complex tasks trigger extended thinking and multi-turn tool calls. The essence of cost control is “spending compute on high-uncertainty steps.”

A simplified pseudo-code illustrates this scheduling approach:

def solve_terminal_task(task, model, budget):
    plan = model.plan(task)            # Lightweight planning, low token cost
    for step in plan.steps:
        if step.uncertainty > THRESHOLD:
            result = model.tool_call(step, budget=budget.extended)
        else:
            result = model.tool_call(step, budget=budget.standard)
        if not verify(result):         # Verifiable signal: exit code / test pass rate
            plan = model.replan(task, history=result.log)
    return plan.execute()

The key lies in the verify step: terminal environments naturally provide verifiable signals (whether compilation passes, whether tests go green). Integrating this into the reasoning loop enables self-correction at a lower cost. This is a primary technical reason such models excel in cost control—not by relying on smaller models, but through more efficient reasoning paths.

Actionable advice for developers: structure failure logs from terminal tasks to build your own regression test suite. When integrating, prioritize the completeness of the “verifiable closed loop” before considering model scale.

III. 3D Content Generation: Scripting Unblocks the Modeling Pipeline

3D content generation is one of the fastest-growing application directions recently, but its technical challenges differ entirely from 2D image generation. While image generation focuses on modeling pixel distributions, 3D generation requires the unification of spatial structure + parametric description + executable operations.

Current mainstream technical approaches fall into two categories: end-to-end generation of 3D representations (point clouds, voxels, implicit fields), and generating executable professional modeling scripts interpreted by host software. The latter offers more engineering advantages—products are editable, parametric, reusable, and naturally integrate with existing modeling toolchains.

In evaluations for 3D spatial reasoning and script generation, a post-trained model achieved a score of 92.7 (with tool dependencies), approaching the level of leading closed-source models and representing the best performance among currently accessible models. Practically, this means the model can directly output scripts for professional modeling environments without an intermediate protocol layer, completing asset staging, refinement, layout adjustments, and material/geometric parameter modifications.

A typical 3D script generation task generally involves the following steps:

1. Scene parsing: Decomposing natural language requirements into object lists, spatial relationships, and style constraints.
2. Parametric modeling: Selecting geometric primitives and modifier combinations for each object, determining size, position, and rotation.
3. Material and lighting: Binding material nodes, setting roughness, metallic, and normal parameters.
4. Layout and composition: Handling occlusion, sightline guidance, and proportions.
5. Export and validation: Outputting printable formats or render sequences, and performing geometric legality checks (manifoldness, normal orientation, minimum wall thickness).

An illustrative script snippet (expressed via a Python API in a generic modeling environment):

import modeling_api as m
scene = m.Scene()
for spec in parse_layout(user_prompt):
    obj = m.create_primitive(spec.kind, size=spec.size)
    obj.apply_modifier("bevel", width=spec.bevel)
    obj.set_transform(loc=spec.loc, rot=spec.rot)
    obj.assign_material(
        base_color=spec.color,
        roughness=spec.roughness,
        metallic=spec.metallic,
    )
    scene.link(obj)
scene.validate_manifold()      # Geometric legality check, mandatory before printing
scene.export("output.stl")

The practical value of these capabilities manifests on three levels:

• Lowering the modeling barrier: Non-professional users can describe their needs in natural language and directly receive editable parametric assets.
• Improving iteration efficiency: Scripting means “change a parameter and rerun” instead of manual rework, ideal for batch variant generation.
• Connecting creation to manufacturing: Models can preview and edit in an online environment, export to print formats, or render videos/dynamic photos, forming a closed loop.

An important engineering caveat: the reliability of 3D script generation heavily depends on geometric legality validation. Models might generate visually plausible but non-manifold meshes, which will fail during printing. Therefore, the pipeline must integrate validation and auto-repair mechanisms, treating “generation” and “manufacturability” separately.

IV. Complex Document Reasoning: Retrieval, Verification, and Hallucination Suppression

Financial document reasoning is another high-value scenario, with technical characteristics distinct from code and 3D: inputs are multi-page real-world documents (financial reports, announcements, contracts), and tasks involve cross-document retrieval, numerical calculation, and multi-step reasoning, with outputs requiring traceability and verifiability.

Evaluations for such tasks typically cover two dimensions: first, complex financial data analysis, involving retrieval positioning, cross-document consistency checks, and multi-step numerical reasoning; second, supporting a complete investment workflow, from target screening to profitability analysis. On a post-trained model, the former achieved a score of 64.4, a current leading position; the latter scored 58.6, a significant improvement over the previous generation.

More noteworthy than the scores is hallucination control. In internal stress tests, the error rate of this version dropped from 23.4% in the previous generation to 8.3%. The technical sources for this reduction can be roughly categorized into three approaches:

• Decoupling retrieval and reasoning: First locate evidence paragraphs, then compute based on the evidence, preventing the model from fabricating numbers from “memory.”
• Process verifiability: Requiring the model to output intermediate calculation steps and cited document locations for external verification.
• Refusal mechanism: Explicitly outputting “cannot be confirmed from the given documents” when evidence is insufficient, rather than providing plausible speculations.

In engineering practice, it is recommended to design the document reasoning system as a dual-layer structure of “model + validator”:

def answer_financial_query(query, docs, model):
    evidence = retrieve(query, docs)              # Retrieval layer, returns paragraphs and coordinates
    if not evidence.sufficient:
        return {"answer": None, "reason": "insufficient_evidence"}
    draft = model.reason(query, evidence)          # Reasoning layer, outputs steps and citations
    checked = verify_numeric(draft.steps, evidence)  # Validation layer, recalculates numbers
    return {"answer": checked.value, "citations": checked.refs}

verify_numeric is key to suppressing hallucinations: all numerical conclusions are recalculated against the original text, and inconsistencies trigger a retry. This architecture is orthogonal to model capabilities and applies to any foundation model.

V. Capability Matrix and Selection Guidelines

Viewing these three tracks together provides a clearer capability matrix:

Dimension	Key Capabilities	Engineering Essentials	Primary Risks
Code Agents	Long-horizon planning, tool calling, error recovery	Verifiable closed loop, tiered inference budget	Long-tail environment differences causing failures
3D Generation	Spatial reasoning, parametric scripting, material layout	Geometric legality validation, parametric reuse	Non-manifold meshes, proportion distortion
Document Reasoning	Cross-document retrieval, multi-step numerical reasoning	Evidence citation, numerical recalculation, refusal	Over-inference when evidence is insufficient

Recommendations for model selection:

1. Define task boundaries before discussing model scores. For terminal tasks, prioritize the completeness of verifiable signals; for 3D tasks, prioritize export formats and validation pipelines; for document tasks, prioritize citation traceability.
2. Treat cost as a first-class metric. At equivalent scores, solutions with shorter reasoning paths and more precise tool calls yield lower long-term operational costs.
3. Maintain replaceability. Make retrieval, validation, and rendering independent modules to avoid deep coupling with a single model, facilitating future iterations.

VI. Conclusion

The technical competition in the post-training phase is essentially about “how to use engineering methods to directionally transform general capabilities into usable capabilities.” Cost optimization in code agents, pipeline unification via direct 3D scripting, and hallucination suppression in document reasoning—these three seemingly disparate paths share a consistent underlying logic: verifiable signals + tiered inference budgets + modular validation pipelines.

For developers, this means there is no need to wait for the “strongest model.” Instead, build your own validation closed loops and evaluation sets early on. Models will continue to iterate, but a well-designed engineering architecture will consistently deliver returns with every foundation model upgrade.