Can Extreme Post-Training Scaling Match a New 3-Trillion Parameter Foundation Model?

The LLM track has been highly active recently. On one hand, new versions of models are constantly刷新ing benchmarks on social networks, with vendors releasing next-generation flagship models claiming to be the “highest-ranked open-source model” with “leading coding capabilities.” On the other hand, a colossal model with nearly three trillion parameters was quietly released, also focusing on coding and agentic capabilities, and positioning itself directly against top-tier international closed-source models.

These two distinct technical routes—refining an existing foundation model through extreme post-training versus building an entirely new, ultra-large parameter foundation model from scratch—collide to form one of the most intriguing aspects of the current LLM competition. This article breaks down both approaches to evaluate the true substance of this showdown.

1. The Protagonists: A “Deep Dive” Rather Than a “Generational Shift”

Let’s start with a conclusion: one recent major update was not a generational shift, but a deep dive.

According to official introductions, the new version features absolutely no changes to the foundation model compared to its predecessor. All efforts were channeled into the post-training stage—elevating the model’s intelligence ceiling through extreme post-training scaling. Specifically, this approach relies on three main pillars:

• Dozens of times more long-horizon task environments: The model is immersed in real engineering task chains for much longer, rather than cycling through quick Q&A pairs.
• Richer and more diverse environment types: Covering scenarios like terminal operations, repository management, and cross-tool collaboration, forcing the model to learn capability transfer across heterogeneous environments.
• Ultra-long post-training time: Brute-forcing compute allocation, trading time for capability space.

This philosophy deserves elaboration. There is an unspoken hierarchy in the industry: changing the foundation model is considered a “true upgrade,” while modifying post-training is dismissed as “tuning.” However, the results prove that when the foundation model’s potential is not exhausted, scaling the reinforcement learning stage can absolutely serve as a primary booster—pre-training determines the model’s lower bound, while post-training dictates how high the ceiling can go.

The official summary of new capabilities includes four items: stronger coding capabilities, cybersecurity capabilities, a post-training scaling methodology, and open-source availability. The first three are capabilities; the last is an attitude. In an era where companies hoard model weights, maintaining an open-source approach is a product strategy in itself.

2. The Alternative Path: The Brute-Force Aesthetics of a 3-Trillion Parameter Foundation Model

In stark contrast to the “unchanged foundation” route is another ultra-large parameter model launched in the same period. Its core selling point is direct: a brand-new foundation model with nearly three trillion parameters, rebuilt from the pre-training stage. It also focuses on coding and agentic capabilities, is open-source, and officially claims its “coding capabilities surpass top-tier international closed-source models.”

The logic behind this route is equally clear: data quality, data ratios, and architectural efficiency during the pre-training stage are the fundamental factors determining the capability ceiling. If the foundation itself is not strong enough, post-training will eventually hit a wall. Rather than patching up an old foundation, it is better to train a “new king” from scratch using a larger parameter scale, a newer architecture, and cleaner data.

However, the cost is equally obvious: training a three-trillion parameter model requires astronomical financial investment, and inference costs are significantly higher than those of mid-sized models. For most developers and enterprises, whether they can afford to run it and deploy it is the true threshold determining the model’s practical value.

3. Technical Breakdown: What Exactly is Post-Training Scaling Doing?

To understand this showdown, we must first clarify what “post-training scaling” actually entails.

Traditional LLM training is divided into three stages: pre-training, supervised fine-tuning (SFT), and reinforcement learning (RL) alignment. Pre-training determines the model’s knowledge reserve and base capabilities, while the latter two are primarily about “alignment” and “elicitation.” However, the trend over the past year shows that the proportion of compute allocated to post-training is rapidly increasing, especially for tasks requiring multi-step reasoning, such as coding, mathematics, and agentic workflows.

The core idea of post-training scaling is: no longer treating post-training as a lightweight finishing step, but as an independently scalable training phase. Specific methods include:

1. Environment Scaling: Constructing a large number of high-quality, interactive, and verifiable task environments where the model can learn through trial and error.
2. Trajectory Scaling: Enabling the model to handle long-horizon dependencies through longer reasoning chains and more rounds of tool calls.
3. Verifier Scaling: Using stronger verification signals (unit tests, static analysis, runtime feedback) to guide reinforcement learning, rather than relying on human annotation.

The key to this methodology is that the quality of the environment and verifiers directly determines the ceiling of post-training. If the environment is too simple or the verification signal too sparse, the model will only learn superficial pattern matching, failing to grasp genuinely transferable problem-solving capabilities.

4. Capability Comparison: True Performance in Coding and Agentic Scenarios

In the two core scenarios of coding and agentic workflows, the two routes exhibit distinct strengths.

In terms of coding capabilities, the post-training scaling route shows significant improvements in tasks like code generation, bug fixing, and cross-file refactoring. This is because coding tasks inherently possess verifiability—whether unit tests pass, whether compilation succeeds, and whether the code crashes at runtime are all clean reward signals. As long as the environment is sufficiently rich and the verifier strict, reinforcement learning can continuously drive the model forward.

Conversely, the ultra-large parameter foundation route holds an advantage in tasks requiring broad knowledge, such as complex system design, long-context understanding, and multi-language code migration. The knowledge capacity brought by three trillion parameters is difficult for mid-sized models to compensate for through post-training alone.

Regarding agentic capabilities, both routes emphasize tool calling, multi-step planning, and environment interaction. However, the post-training scaling route focuses more on eliciting agentic behavior on top of an existing foundation, while the new foundation route injects massive amounts of agentic trajectory data starting from the pre-training phase. Theoretical limits are higher, but so are training costs.

5. Engineering Practice: How Should Developers Choose?

For developers and enterprises, choosing which route depends on specific application scenarios and resource constraints.

If you pursue an ultimate coding assistance experience and your deployment environment can accommodate higher inference overhead, the ultra-large parameter foundation model might be the better choice. It offers stronger generalization on complex tasks and better coverage of long-tail scenarios.

If you prioritize cost-efficiency and deployment flexibility, a mid-sized model subjected to extreme post-training scaling is more worth considering. Its inference and fine-tuning costs are lower and more controllable, and in vertical scenarios like coding and agentic workflows, the perceived performance gap may be far smaller than the difference in parameter sizes.

If your team has reinforcement learning engineering capabilities, you can also consider performing post-training scaling on an open-source foundation, tailored to your specific business scenarios. For example:

# Pseudo-code: Core loop of post-training scaling
for epoch in range(num_epochs):
    # 1. Sample from the task environment
    trajectories = env.sample_batch(policy_model)
    # 2. Score with the verifier
    rewards = verifier.score(trajectories)
    # 3. Update the policy
    policy_model.update(trajectories, rewards)
    # 4. Evaluate periodically to prevent capability degradation
    eval_score = evaluate(policy_model, held_out_tasks)

The crux of this pipeline lies in environment construction and verifier design, not in the size of the model itself. In other words, the barrier to entry for post-training scaling is not raw compute, but engineering capability.

6. Conclusion: Not a Replacement, but a Two-Legged Approach

Returning to the original question: Can extreme post-training scaling on an unchanged foundation defeat a new 3-trillion parameter king?

The answer is not a simple “yes” or “no.” The two routes have their own applicable boundaries: post-training scaling excels at mining potential from an existing foundation, offering controllable costs, rapid iteration, and flexible deployment; ultra-large parameter foundations excel at breaking capability limits, offering strong generalization, broad knowledge, and higher ceilings.

The truly rational strategy is a two-legged approach: use ultra-large parameter models to explore the boundaries of capability, and use post-training scaling to distill mature capabilities into smaller, cheaper models. For most developers and enterprises, the best model is the one that delivers the optimal performance in their specific scenario at an acceptable cost.

This showdown has no losers. It simply reinforces one fact: in the era of large language models, training methodology and engineering capability are becoming just as important as parameter scale.