Beyond Base Models: How Post-Training and RL Optimize LLMs for Coding and 3D Vision

Over the past six months, the open-source Large Language Model (LLM) landscape has experienced a quiet but fundamental shift in focus: moving away from “who has the largest base parameters” toward “who has the most robust post-training.” A landmark example of this is an open-source model that, while maintaining its existing ultra-large sparse architecture, achieved an 11-point increase in its overall intelligence index through just over a hundred days of large-scale reinforcement learning (RL) post-training. This propelled it into the global top tier while simultaneously reducing API costs by approximately one-third. The takeaway for developers is straightforward: the base model determines the ceiling, but post-training determines the practical floor you can actually leverage.

This article does not review or recommend specific products. Instead, it abstracts this phenomenon into a reusable technical question: When the base model is already sufficiently large, how can we combine RL, toolchain refinement, and inference cost optimization to make the model genuinely usable in hard scenarios like coding, 3D, and front-end vision? We will explore this across five aspects: timeline review, capability jump mechanisms, vision and front-end performance, cost structure, and engineering implementation.

1. Timeline Review: What Actually Happened in Three Months

By abstracting the key milestones, a clear dual-track strategy of “peripheral iteration + internal refinement” emerges:

Phase	Action Type	Technical Implication
Month 0	Release of ultra-large open-source base model	Validated low-level engineering like operator optimization and compilation principles, but real CLI task pass rate was only ~13%
Months 1—2	High-speed iteration of peripheral toolchain	Encapsulated CLI capabilities into GUIs: built-in browser, visual annotation, plan/goal dual modes, paving the way for long-horizon Agents
Month 2	Community warm-up and anonymous trials	Collected real adversarial data via anonymous channels on public arenas for subsequent preference optimization
Month 3	Launch of the finalized post-trained version	Committed to releasing full weights; API price dropped by ~1/3

The most notable aspect here is not the release of a new version, but rather the strategic decision not to rush into building a larger base model. With limited compute budgets, investing resources in RL post-training and toolchain refinement yields more certain returns than continuously scaling parameters. The reason is clear: the bottleneck in real-world programming tasks is rarely whether the model “knows a specific API,” but rather whether it can “maintain goal consistency across multi-turn tool calls.” This is precisely the problem that post-training and Agent frameworks aim to solve.

2. Mechanisms of Capability Jumps: Why Joint RL is Effective

Jumping from an intelligence index in the 44-point range to the 55-point range and entering the global top four is rarely achieved through standard fine-tuning. The underlying mechanism can be broken down into three layers:

1. Joint Domain Reinforcement Learning. Single-domain RL often leads to imbalanced capabilities: strong math might result in weak code, or strong code might degrade instruction-following. “Joint RL across a hundred domains” essentially involves joint sampling and gradient coordination among multiple verifiable-reward task families, allowing the model to maintain multiple capabilities simultaneously within a shared representation. For developers, this means a single model can write backend logic, handle frontend layouts, and create data cleaning scripts without needing a separate small model for each scenario.

2. Real Environment Rewards. The reward signal for programming tasks must come from real execution results—whether it compiles, whether unit tests pass, whether rendering meets expectations—rather than from human-annotated preference pairs. These rewards have low noise and high scalability, which is the key to elevating Terminal-style benchmark pass rates from the 13% range to significantly higher levels.

3. Long-Horizon Consistency Training. In Agent scenarios, a model must execute dozens of sequential steps: read files, modify code, run tests, read errors, and modify again. If post-training only optimizes single-step outputs, the model will “forget the goal” after the third step. Long-horizon consistency training significantly improves this by using the final result of the entire trajectory as the reward.

The pseudo-code training loop can be abstracted as follows:

for domain in joint_domains:            # Joint sampling across 100+ domains
    trajectory = rollout(policy, env=domain.env)
    reward = execute_and_verify(trajectory)   # Real execution + verifiable reward
    loss = ppo_loss(policy, trajectory, reward)
    loss.backward()
# Key: Share gradients across domains to avoid capability imbalance

3. 3D and Front-End Vision: From “Writing” to “Seeing”

The performance of this generation of post-trained models on vision-related tasks has been an unexpected highlight for many developers. This is primarily reflected in two areas:

• Front-End Restoration. Given a UI screenshot or a text description, the model can generate structurally clear and semantically correct markup and styling code, maintaining layout consistency across multiple rounds of revisions. This relies on joint training of “visual annotation + code generation”: the model must understand not just pixels, but also the box model, stacking contexts, and responsive breakpoints.
• 3D Scene Generation. Generating 3D scene structures, materials, and camera parameters through natural language descriptions, and exporting them to standard formats. For independent developers and small teams, this significantly lowers the barrier for prototyping—tasks that previously required a dedicated 3D artist can now be rapidly iterated through scripted methods.

It is important to note that these capabilities are currently more suited for prototyping and low-to-medium complexity scenarios. High-fidelity rendering, complex shaders, and physics simulations still require professional toolchains. A sound engineering practice is to treat the model’s output as a “first draft” and then feed it into existing resource pipelines for validation and optimization.

4. Cost Structure: Why the Price Dropped by One-Third

The claim of achieving flagship-level coding capabilities at a fraction of the cost stems from three main factors:

1. Sparse Activation Architecture. Although the base model has a massive parameter count, each inference only activates a small subset of experts. The actual compute and memory footprint are significantly lower than a dense model with an equivalent total parameter count.
2. Post-Training Over Parameter Expansion. By not continuously scaling up the base model, the most expensive one-time training costs and subsequent inference costs were directly saved.
3. Toolchain Caching and Batching. In Agent scenarios, system prompts, tool definitions, and historical trajectories can be effectively reused. Combined with prefix caching and request batching, the token cost per task decreases noticeably.

For teams, the absolute price is less meaningful than the cost per task: how many tokens, how many tool calls, and how many retries are needed to complete the same bug fix. The pass rate improvement brought by post-training directly reduces the number of retries, which is the primary source of cost reduction.

5. Engineering Implementation Recommendations

If you are evaluating the integration of such models into your R&D workflow, consider the following sequence:

• Establish a small-sample baseline. Select 20—50 real tasks (e.g., fixing bugs, writing unit tests, restoring UIs) and record pass rates and average turns as a benchmark for future comparisons.
• Prioritize verifiable environments. Giving the model a sandbox where it can run tests and read errors is more effective than simply optimizing prompts.
• Scenario-based model selection. Use smaller models for high-throughput code completion and flagship models for complex refactoring and Agent tasks, unified through a routing layer.
• Monitor weight release schedules. Once full weights are available, evaluate private deployment and data compliance options, especially for teams dealing with sensitive codebases.
• Build a regression set. Run the same set of tasks for every model upgrade to avoid the trap of “benchmarks went up, but my specific scenario didn’t.”

Conclusion

The most critical signal from this recent evolution of open-source models is that post-training is becoming a more critical variable than parameter scale. When selecting models, developers should not fixate solely on base model size. Instead, focus on three more practical questions: real task pass rate, toolchain maturity, and cost per task. Quantifying these three metrics clearly is the only way to make stable, reusable technical decisions in a rapidly evolving model ecosystem.