Post-Training Compute Scaling Unlocks the Potential of Very Large Models

1. Background: Pre-Training Is No Longer the Only Capability Curve

For the past several years, large model capability improvements have been tightly coupled with pre-training scale: more parameters, more data, and more compute generally led to better performance. However, as models enter the multi-trillion-parameter range, relying solely on another round of larger pre-training is producing diminishing returns. Training costs are rising exponentially, while the capability improvement curve is gradually flattening.

This raises a more engineering-focused question: when a base model is already strong enough, how can post-training be used to fully unlock its potential?

One technical direction worth attention is the following: reuse an existing base model, substantially increase post-training compute, scale reinforcement learning to hundreds of downstream domains, and optimize the training framework, long-context inference, sandbox environments, and task scheduling systems. This approach delivers significant capability gains without another pre-training run. For developers and enterprise users, it implies faster model iteration cycles and a more controllable cost structure.

2. Core Idea: Post-Training as the Main Battleground for Capability Release

2.1 Why Post-Training

Pre-training gives a model general language understanding and generation ability. It is a form of knowledge compression across broad domains. Real-world tasks, however, often require multi-step, verifiable, tool-interactive operations within specific domains. These capabilities are difficult to obtain through pre-training alone. They must be shaped through repeated trial and feedback during post-training.

The core advantages of post-training include:

• Controllable cost: reuse an existing base model instead of repeatedly investing massive pre-training compute.
• Faster iteration: quickly experiment with and tune specific capability dimensions.
• Strong verifiability: real tasks provide natural feedback signals, which makes it easier to build reinforcement learning loops.

2.2 Scaling Reinforcement Learning to Hundreds of Downstream Domains

A key practice is extending reinforcement learning from a few domains to hundreds of downstream domains. Each domain provides its own task distribution, reward signal, and verification mechanism. Examples include:

• Coding tasks: rewards based on whether tests pass or whether compilation succeeds.
• Document analysis tasks: rewards based on whether extracted fields are accurate and whether answers can be traced back to source content.
• 3D modeling tasks: rewards based on whether model structures are valid and render correctly in the target engine.

The essence of this multi-domain parallel reinforcement learning approach is to help the model learn general problem-solving strategies across many heterogeneous tasks, rather than memorizing patterns from a single domain.

2.3 Long-Context Inference and Task Scheduling

When context length reaches the million-token level, inference faces new challenges: attention computation overhead, KV cache management, and information localization within long documents. Common engineering practices include:

• Chunking long contexts and applying retrieval augmentation to reduce unproductive attention overhead.
• Optimizing inference scheduling so long tasks and short tasks are interleaved sensibly, improving throughput.
• Isolating task execution in sandbox environments to keep tool calls safe and controllable.

These system-level optimizations are as important as the post-training algorithms themselves. Together they determine whether model capabilities can be delivered reliably in real-world scenarios.

3. Capability Validation: From Terminal Coding to Professional Document Analysis

3.1 Real Terminal Coding Tasks

On benchmarks that evaluate real terminal coding tasks, post-trained and reinforcement-optimized models perform strongly in loops that involve modifying code, running tests, and continuing to adjust based on error messages. These tasks typically require:

1. Understanding the structure of an existing codebase.
2. Executing commands and observing output.
3. Iteratively fixing issues based on failure information.

A typical interaction loop looks like this:

# The model runs the tests
$ run_tests
FAILED test_parser.py::test_edge_case
# The model reads the error and locates the code
$ cat parser.py | grep -n "edge_case"
# The model modifies the code and reruns the tests
$ run_tests
PASSED

This execute-observe-correct ability is a direct result of repeated reinforcement learning in real environments during post-training.

3.2 Professional Document Analysis

In professional document analysis scenarios such as finance, models need to extract key metrics from long reports, verify data consistency, and produce traceable conclusions. These tasks place high demands on long-context understanding and precise retrieval. Domain-specific reward signals introduced during post-training can significantly improve accuracy.

3.3 3D Modeling and Tool Calling

The model can also call professional modeling tools, computer-aided design tools, and game engines to complete complex 3D modeling, scene development, and multi-step software engineering tasks. This requires the model not only to understand natural language instructions, but also to generate structured tool-call parameters and decide the next step after receiving tool output.

4. Engineering Recommendations

For teams that want to replicate a similar post-training approach in their own systems, the following points are worth considering:

• Build verifiable task environments: each downstream domain should have clear success/failure criteria so that effective rewards can be formed.
• Sandbox isolation: tool calls and code execution must run in isolated environments to avoid security risks.
• Long-context optimization: use retrieval and chunking strategies appropriately to balance context length and inference cost.
• Scheduling system support: multi-domain reinforcement learning generates many heterogeneous tasks, requiring efficient scheduling and resource management.
• Continuous evaluation: look beyond single benchmark scores and track end-to-end completion rates on real tasks.

5. Conclusion

Unlocking the potential of very large models is shifting from larger pre-training runs to stronger post-training. By scaling reinforcement learning across hundreds of downstream domains and optimizing long-context inference, sandbox environments, and task scheduling systems, models can make meaningful progress in coding, agents, 3D modeling, and professional document analysis. For developers, this points to a more engineering-driven and reusable capability iteration path, as well as a clearer technical direction for building applications that target real business scenarios.