The Evolution of Open-Source Model Post-Training: From Code Terminal Tasks to 3D Scripting

I. Background: Post-Training as the Key Variable in Model Differentiation

Over the past year, the focus of competition among large language models has shifted significantly. While the pre-training phase establishes a model’s “knowledge base” and general language capabilities, the underlying architectures are increasingly converging. In this context, post-training—encompassing supervised fine-tuning, preference alignment, reinforcement learning, and tool-use training—has become the critical factor in widening the performance gap.

A typical observation is that the same foundation model, after undergoing different post-training strategies, can exhibit score differences of tens of points on specific tasks. This explains why a model provider can achieve a significant leap on vertical task leaderboards months after a foundation model’s release, simply through a post-training-focused version update. The attention garnered by such an update often rivals that of an entirely new foundation model release.

This article focuses on “capability enhancement driven by post-training” and explores two main themes:

1. Code Terminal Tasks: The model’s ability to execute multi-step engineering tasks within real command-line environments, along with the associated cost-efficiency considerations.
2. 3D Modeling and Scripting: The model’s ability to directly write scripts for professional modeling software to construct, refine, and layout assets without relying on specialized tool interfaces.

The commonality between these two themes is that they both transcend “single-turn Q&A.” They represent long-horizon, multi-step tasks reliant on external tool feedback—precisely the scenarios where post-training proves most effective.

II. Code Terminal Tasks: Balancing Capability Limits and Cost Curves

2.1 Why Terminal-Style Benchmarks Are Harder

Unlike traditional function-level code completion benchmarks, terminal-style benchmarks evaluate a model’s comprehensive performance in a simulated real shell environment. Typical tasks include:

• Reading error messages and locating root causes;
• Making consistent modifications across multi-file projects;
• Executing commands, observing outputs, and adjusting strategies based on feedback;
• Converging to a verifiable correct result within a limited number of steps.

The difficulty of these tasks lies not in “writing a single correct line of code,” but in state management. The model must maintain continuous awareness of the file system, dependencies, and execution results. A misjudgment at any step can trigger a cascading failure in subsequent steps. Consequently, improving scores on terminal-style benchmarks is generally harder than on standard coding benchmarks, offering a better reflection of a model’s usability in real-world engineering scenarios.

2.2 How Post-Training Enhances Terminal Tasks

Technically, improving performance on terminal-style tasks typically relies on the following post-training methods:

• Trajectory-Level Supervision: Fine-tuning with extensive “command-output-correction” trajectories, enabling the model to adjust actions based on environmental feedback rather than generating a one-shot answer.
• Reinforcement Learning with Verifiable Rewards: Using whether a task passes test cases as a reward signal to guide the model toward more robust problem-solving paths.
• Tool Call Format Alignment: Standardizing the formats for command construction, output parsing, and error retry to reduce wasted steps from invalid interactions.

The combined result of these methods is an improvement in the model’s step efficiency for long-horizon tasks. Fewer invalid attempts simultaneously improve the success rate and reduce costs.

2.3 Cost Efficiency: An Underestimated Core Metric

In engineering practice, the cost of terminal tasks consists of three main components: input tokens (context and historical trajectories), output tokens (commands and reasoning), and additional rounds caused by retries. Even if a model has a high success rate, its actual usage cost can escalate rapidly if it requires numerous retries to converge.

Therefore, evaluating these models requires a two-dimensional perspective: the x-axis represents the task pass rate, and the y-axis represents the average cost per task. The ideal position is the bottom-right quadrant: “high pass rate + low cost.” Some lightweight models have a lower unit price, but due to insufficient pass rates and increased retries, their end-to-end costs are actually higher—a common pitfall in model selection.

A simplified estimation method can be used for comparison:

# End-to-end single task cost estimation
def cost_per_task(price_in, price_out, avg_in_tokens, avg_out_tokens, avg_retries):
    single_round = (avg_in_tokens * price_in + avg_out_tokens * price_out)
    # Retries accumulate context, and cost scales roughly linearly with rounds
    return single_round * avg_retries
# During selection, compare cost_per_task / success_rate,
# i.e., the "actual cost per successful task," rather than just the unit price.

For enterprises, this perspective implies that cost optimization does not mean selecting the model with the lowest unit price, but the one with the lowest cost per successful task. In scenarios like automated pipelines, bulk refactoring, and CI-assisted repairs, this difference is significantly magnified.

2.4 Engineering Implementation Recommendations

For teams looking to integrate terminal capabilities into their R&D workflows, the following sequence is recommended:

1. Establish a small-sample baseline: Extract 30 to 50 real repository tasks to build a dual-metric baseline for pass rate and cost.
2. Define tool boundaries: Clearly specify the range of commands the model can execute and sandbox constraints to avoid uncontrolled side effects.
3. Introduce a verification loop: Use test cases or static checks for automated acceptance to reduce manual review costs.
4. Record and review trajectories: Use failure trajectories as input for subsequent prompt engineering and workflow optimization.

III. 3D Modeling Script Generation: From “Calling APIs” to “Direct Scripting”

3.1 The Shift in Capability Positioning

3D content creation has long faced a high barrier: the operational complexity of professional modeling tools and a steep learning curve. Previously, the common approach for models to participate in 3D creation was relying on specialized tool APIs or plugin protocols (e.g., exposing modeling capabilities via standardized model context protocols), with external services performing the actual geometric operations.

The current clear trend is that models are beginning to bypass the middleware layer and directly generate native scripts for modeling tools. This means the model must understand the target software’s scripting API, object model, coordinate system conventions, and modifier semantics, organizing this knowledge into an executable program.

The technical significance of this shift includes:

• Reducing reliance on specialized APIs, thereby lowering integration costs;
• Scripts are inherently versionable and parameterizable, facilitating batch generation and reuse;
• Models can execute multi-stage tasks such as “construction-refinement-layout-materials” rather than single-step operations.

3.2 Spatial Reasoning is the True Bottleneck

The difficulty of 3D script generation lies not in syntax, but in spatial reasoning. A viable modeling script requires the model to simultaneously handle:

• Geometric relationships: Relative positions, orientations, and scale ratios between objects;
• Topological constraints: Boolean operation sequences, normal directions, and mesh closure;
• Parameter linkage: Associations between materials, lighting, cameras, and geometry;
• Reproducibility: Repeated script execution should yield stable results.

This explains why tool usage significantly impacts scores in CAD-style benchmarks—when tools are allowed, models can leverage geometry kernels for validation; without tools, it entirely tests the model’s own spatial modeling intuition. The fact that open-source models can approach the level of cutting-edge closed-source models in this dimension indicates that specialized data construction for spatial reasoning during post-training has been effective.

3.3 An Illustrative Script Generation Workflow

Taking a common modeling script generation process as an example, a robust workflow typically consists of four stages:

# Stage 1: Structured Requirement Parsing
# Input natural language description, output structured scene graph
scene_spec = {
    "objects": [
        {"type": "table", "size": [1.2, 0.8, 0.75], "position": [0, 0, 0]},
        {"type": "chair", "count": 4, "arrange": "around", "radius": 1.0},
    ],
    "materials": {"table": "wood", "chair": "fabric"},
    "camera": {"angle": "three_quarter", "height": 1.6},
}
# Stage 2: Script Skeleton Generation
# Translate the scene graph into a sequence of modeling software script calls
# Stage 3: Parameter Refinement and Constraint Validation
# Check for size conflicts, overlaps, normal directions, etc.
# Stage 4: Reproducibility Verification
# Execute the script repeatedly, comparing the consistency of output geometry

In practice, it is recommended to implement “parameter refinement” and “constraint validation” as independent, reentrant steps, rather than having the model output the complete script in one go. This way, if an error occurs in a specific step, only local regeneration is required, improving both cost efficiency and controllability.

3.3 Implementation Paths for Creative Scenarios

For content creation teams, the value of 3D script generation capabilities primarily manifests in three scenarios:

• Batch Asset Generation: Using parameterized scripts to mass-produce variants for scene population and style exploration;
• Layout and Composition Iteration: Rapidly generating multiple layout options for art teams to filter and refine;
• Material and Lighting Presets: Scripting common configurations to form an internal asset template library for the team.

It is important to note that the output quality of generated scripts heavily depends on the standardization of the input. In practice, the most effective preliminary step is to write requirements as structured descriptions with numerical constraints rather than vague natural language. For instance, “table height 0.75m, chair surrounding radius 1m” is much more likely to yield an executable result than “make it look a bit coordinated.”

IV. Comprehensive Evaluation: Assessing the Value of “Post-Trained Versions”

4.1 Three Evaluation Dimensions

When faced with a post-training-focused version update, a rational evaluation should be conducted across three dimensions:

1. Capability Dimension: The absolute performance on target tasks, and the relative position among similar open-source models;
2. Efficiency Dimension: The cost per successful task, including hidden overheads from retries and context accumulation;
3. Engineering Dimension: The standardization of tool call formats, the parsability of error feedback, and compatibility with existing pipelines.

All three are indispensable. Comparing only leaderboard scores tends to ignore efficiency; comparing only unit prices ignores pass rates; and ignoring engineering standards will incur additional costs during the integration phase.

4.2 Limitations and Risks

Objectively speaking, 3D script generation at its current stage still has clear boundaries:

• Complex Assemblies: In scenarios with strong coupling between multiple parts, the model’s handling of constraint propagation remains unstable;
• Non-Standard Workflows: For processes relying on specific plugins or custom nodes, the model lacks reliable priors;
• High Geometric Precision Scenarios: Engineering drawing-level precision still requires manual verification and fallback geometry kernels.

Therefore, a reasonable positioning is to treat the model as a “script draft generator” and a “batch variant generator” rather than a complete replacement for professional modeling workflows. The human-in-the-loop review process will remain necessary for a considerable time.

4.3 Practical Recommendations for Teams

• Establish a task grading mechanism: Classify tasks by complexity into “directly adoptable,” “requires light review,” and “reference only,” configuring different models and validation strategies for each;
• Consolidate an internal script template library: Abstract validated generated scripts into templates to lower the generation difficulty of subsequent tasks;
• Focus on observability: Record the inputs, outputs, execution results, and manual modifications of each generation to form a continuously optimizable data loop;
• When deploying in the cloud, integrate general cloud capabilities (such as using object storage to save scripts and assets, container platforms to handle batch rendering tasks, and logging services to collect execution trajectories) to build a complete pipeline.

V. Conclusion

Post-training is pushing the capability boundaries of open-source models from “general conversation” to “executable long-horizon tasks.” Code terminal tasks test a model’s state management and convergence efficiency in real environments, while 3D script generation tests spatial reasoning and tool semantic understanding. Both point to the same conclusion: evaluating a model requires looking not just at what it can do, but at what cost and under how stable conditions it achieves it.

For developers and enterprise users, true value lies not in leaderboard rankings, but in embedding these capabilities into verifiable, rollback-capable, and observable engineering workflows. When model-generated scripts can be automatically validated and terminal task failure trajectories systematically reviewed, the capability enhancements brought by post-training will truly transform into productivity.