RL Post-Training and the Evolution of Visual Programming Capabilities in Large Models

1. Introduction: From Parameter Competition to Precision Training

Over the past two years, large language models have been caught up in an arms race measured primarily by parameter count. However, as model sizes approach the trillion-parameter threshold, the marginal benefit of simply adding more pre-training data and compute is declining. The industry is shifting toward a more practical question: how can existing base models release stronger real-world productivity through more refined training strategies and more effective post-training methods, without blindly increasing model scale?

Recently, a model based on a 2.8-trillion-parameter open-source base and fine-tuned through 99 days of reinforcement learning has drawn widespread attention. It has risen to the top tier of global comprehensive intelligence benchmarks, shown notable advantages in areas such as 3D visual understanding and front-end code generation, and significantly reduced API costs compared with comparable flagship offerings. This article does not focus on any single product. Instead, it uses this technical context to examine the methodology of reinforcement learning post-training for large models, the technical paths behind the evolution of visual and programming capabilities, and how enterprises and developers can obtain flagship-level AI coding support at lower cost.

2. Reinforcement Learning Post-Training: The Core of 99 Days of Re-Training

2.1 Why Post-Training Matters More Than Pre-Training

Pre-training answers the question of what a model knows. During this stage, the model learns language patterns and world knowledge from massive text corpora. Post-training, especially reinforcement learning, addresses a different problem: how to use that knowledge effectively. The model must learn to follow instructions, plan tasks, and make correct decisions in complex environments.

A typical code generation scenario illustrates the gap. A pre-trained model may know the syntax of a programming language, but it often struggles when faced with real terminal operations, multi-step debugging, or long-running agent tasks. This is precisely the bottleneck that reinforcement learning post-training aims to overcome.

2.2 The Technical Framework of Joint RL Re-Training

The so-called joint RL re-training across more than one hundred domains is essentially a multi-task, multi-scenario reinforcement learning fine-tuning strategy. Its technical framework typically includes the following key components:

1. Reward Model Construction

Reward models are designed or jointly trained for different task domains such as code generation, visual understanding, mathematical reasoning, and terminal operations. Reward signals may include:

• Unit test pass rate for coding tasks
• Rendering fidelity and interaction smoothness for front-end or 3D tasks
• Task completion rate and step accuracy for agent tasks

# Reward signal example: multi-dimensional weighted scoring
def composite_reward(code_pass_rate, visual_fidelity, step_accuracy):
    w1, w2, w3 = 0.4, 0.3, 0.3
    return (w1 * code_pass_rate + 
            w2 * visual_fidelity + 
            w3 * step_accuracy)

2. Policy Optimization and the Data Flywheel

During RL training, the model continuously generates samples, receives reward feedback, and updates policy parameters. At the same time, high-quality generated samples are recycled as new training data, forming a generate-evaluate-optimize-regenerate data flywheel. A 99-day training period means this cycle can iterate tens of thousands or even hundreds of thousands of times.

3. Distribution Alignment and Safety Constraints

Reinforcement learning can easily lead to reward hacking, where the model finds shortcuts that bypass the actual goal. Post-training therefore needs mechanisms such as KL divergence constraints and safety filtering layers to ensure that model outputs are both effective and controllable.

2.3 From 13% to Flagship Level: Crossing the Terminal Benchmark

On terminal benchmarks that test real command-line operation capabilities, the initial 2.8-trillion-parameter base model scored only around 13%. After 99 days of RL re-training, the same base model achieved several-fold or even tenfold improvements on similar tasks. This leap did not come from adding parameters. It came from:

• Fine-grained reward modeling for terminal operation sequences
• Targeted optimization of error recovery and context retention
• Intermediate step verification mechanisms in long-chain reasoning

3. 3D Vision and Front-End Generation: From Seeing to Building

3.1 The Evolution of Visual Understanding

Traditional multimodal large models are already quite mature at image understanding, but they still have clear weaknesses in 3D spatial understanding and interactive front-end generation. New-generation models achieve breakthroughs through the following technical paths:

1. Spatial Encoding and Rendering Alignment

3D scene information is encoded into token sequences that the model can process, while rendering consistency loss functions are introduced to ensure that the visual content generated by the model follows physical rules in terms of geometric structure and lighting relationships.

2. Direct Front-End Code Output

Instead of merely describing a visual scene, the model directly generates runnable front-end code such as HTML/CSS/JavaScript or mainstream framework code, enabling end-to-end conversion from visual intent to interactive interface. This requires the model to have:

• Deep understanding of UI component libraries and layout specifications
• Adherence to responsive design and accessibility standards
• Implicit awareness of real-time rendering performance optimization

3.2 Why Aesthetic Judgment Becomes a Competitive Advantage

In practical applications, whether generated visuals look good is not purely subjective. It can be quantified through technical indicators such as color harmony, layout balance, interaction smoothness, and brand consistency. Reinforcement learning post-training introduces human preference data through RLHF or AI feedback through RLAIF, gradually aligning the model’s aesthetic output with professional design standards.

For developers, this means an AI-assisted tool can not only write code but also provide a professional-grade first draft at the visual level, substantially reducing the cost of converting design mockups into runnable interfaces.

4. Practical Paths for Programming Capability Gains and Cost Optimization

4.1 The Technical Logic Behind Programming Benchmark Improvements

Programming capability gains are the most direct reflection of post-training RL. Core strategies include:

1. Sandboxed Execution Environments

Code sandboxes are introduced during training so that every model generation can be executed in an isolated environment. Execution results such as successful compilation, passing tests, and error-free runs serve as reward signals. This reflects real programming ability more accurately than static code evaluation alone.

2. Long-Horizon Task Planning

A dual Plan/Goal mode is introduced:

• Plan mode: first formulate the overall architecture and step-by-step plan, then implement incrementally
• Goal mode: directly target the final objective and autonomously decompose sub-tasks

This dual-mode design enables the model to handle requirements ranging from scripts of a few dozen lines to projects with thousands of lines.

3. Toolchain Integration

Browser operations, file system access, and terminal command execution are exposed to the model through APIs. The model is no longer a pure text generator but a programming agent capable of interacting with real environments.

4.2 The Economics of Obtaining Flagship Capability at One-Fifth the Cost

The original text mentions that API prices were reduced by another third. Combined with broader industry trends, the decline in large model API costs mainly comes from:

Cost Driver	Mechanism
Inference optimization	Operator fusion, quantization deployment, and KV cache optimization reduce per-inference compute consumption
Distillation and pruning	Distilling smaller models from larger ones preserves flagship-level capability in key scenarios
Elastic scheduling	Cloud-based on-demand scaling avoids wasted fixed resources
Caching strategies	Semantic caching for high-frequency repeated queries reduces redundant computation

For enterprise users, this means they can deploy intelligent systems with flagship-level coding assistance at one-fifth or even lower of the original budget. Model inference services and containerized deployment solutions provided by platforms such as Baidu AI Cloud offer infrastructure support for realizing such low-cost, high-capability deployments.

5. Developer Practice: Deploying Flagship-Level AI Coding Support

5.1 Technology Selection Recommendations

When evaluating large model coding assistance solutions, enterprises and individual developers should focus on the following dimensions:

1. Base model capability: prioritize large open-source or open-weight models that have undergone thorough post-training, rather than relying solely on pre-training capability
2. Toolchain completeness: whether the model supports agent capabilities such as sandbox execution, terminal operations, and browser interaction
3. Cost controllability: API unit price, token billing granularity, and batch-call discounts
4. Compliance and security: whether code generation includes sensitive information filtering and whether outputs are auditable

5.2 Typical Application Architecture

+-------------------------------------------------+
|       Developer Interaction Layer (IDE/CLI/Web)  |
+-------------------------------------------------+
|       Agent Orchestration Layer (Task Decomposition / Scheduling) |
+------------------+------------------------------+
| Code Generation  | Visual Generation Model      |
| Model            | (Optional)                   |
+------------------+------------------------------+
| Sandbox Execution Environment / Rendering Engine / Tool APIs |
+-------------------------------------------------+
| Inference Service Layer (Elastic Scaling / Quantization Acceleration) |
+-------------------------------------------------+

5.3 Best Practices

• Adopt incrementally: first test AI coding assistance in non-core modules, then expand after validating results
• Define human-machine collaboration boundaries: clarify which parts are AI-generated and which require manual review, especially in security-sensitive code
• Evaluate continuously: build internal benchmark test sets and periodically compare model output quality and efficiency gains

6. Summary and Outlook

From a 2.8-trillion-parameter base model to flagship-level performance after 99 days of RL re-training, this technical path clearly shows that the competitiveness of large models is shifting from who has more parameters to who trains more precisely. Breakthroughs in visual aesthetic capability, progress on programming benchmarks, and substantial cost optimization together form the core competitiveness of next-generation AI coding tools.

For developers, understanding the principles of reinforcement learning post-training, mastering the collaborative use of multimodal generation and code generation, and making good use of cloud-based elastic inference resources are key to maintaining a productivity advantage in this wave of technological change. Going forward, as open weights become more accessible and inference costs continue to decline, flagship-level AI coding capability will no longer be the exclusive domain of a few companies. It will become a standard tool for every developer.