Post-Training Scaling vs. New Foundation Models: How Large Language Models Are Making Their Next Leap

The evolution of large language models is currently splitting into two distinct technical paths. One path starts from scratch by building entirely new foundation models with ultra-large parameter scales, such as trillion-level architectures, to push the boundaries of intelligence. The other path keeps the existing base model intact and shifts computational and engineering resources to the post-training stage, attempting to squeeze out the model’s upper limit through extreme optimization. The collision of these two approaches forms one of the most compelling technical storylines in today’s LLM competition. This article sets aside specific brands and products to examine the core logic and practical differences between these two paths from a general technical perspective.

1. Diverging Technical Routes: Rebuilding Foundations vs. Scaling Post-Training

The capabilities of a large language model are typically determined by two stages: knowledge absorption during pre-training and capability alignment and activation during post-training. Around these two stages, the industry currently shows two mainstream engineering approaches.

1. Building Ultra-Large New Foundation Models

This approach relies on raw scale. It significantly expands the number of model parameters, moving from hundreds of billions to trillions or more, while introducing even larger volumes of high-quality data during pre-training. The underlying assumption is that quantitative growth in parameter scale can trigger qualitative improvements in model capability, producing native advantages in complex logical reasoning and long-range dependency modeling. However, this route is extremely expensive. It requires massive compute clusters for pre-training and also leads to substantially higher alignment and inference costs afterward.

2. Extreme Post-Training Scaling

The alternative view holds that current base models already have enough knowledge capacity, and poor performance is often the result of capabilities that have not been effectively activated. Instead of replacing the base model, the engineering focus is placed entirely on post-training, using techniques such as reinforcement learning to push the model toward its upper limit. This approach challenges the old assumption that only changing the base model counts as a real upgrade. It demonstrates that post-training can serve as a first-stage rocket for raising the model’s intelligence ceiling.

2. Extreme Optimization Without Changing the Base Model: How Post-Training Becomes the First-Stage Rocket

If the base model is left unchanged, what engineering work is needed during post-training to achieve a meaningful capability jump? Based on industry best practices, the key efforts focus on three areas.

1. Building Long-Horizon Task Environments at Much Greater Scale

Traditional post-training often depends on single-turn or limited-turn instruction tuning. Modern complex agents, however, require models that can operate continuously across realistic engineering task chains. By building long-horizon task environments and letting models spend more time in multi-step, cross-tool interactions, developers can improve the model’s ability to maintain context state and recover from errors. For example, in a code repair task, the model not only needs to write code but also needs to revise itself over multiple turns based on runtime error messages.

2. Covering a Rich Variety of Environment Types

Post-training environments should go beyond simple sandbox execution. They need to cover terminals, code repositories, cross-tool collaboration, and other realistic scenarios. This multi-environment coverage strengthens the model’s generalization ability and helps it perform reliably when facing different system calls and API interactions.

3. Stacking Longer Post-Training Time and Compute

Straightforwardly increasing compute investment during post-training is a direct way to deepen model exploration. Through longer reinforcement learning exploration, the model can discover more reward pathways and find better solutions for complex tasks.

The following conceptual pseudo-code shows the scheduling logic for long-horizon task post-training with multi-environment coverage:

# Conceptual pseudo-code: long-horizon post-training scheduling logic
class LongHorizonPostTrainer:
    def __init__(self, base_model, environments):
        self.model = base_model
        # environments includes terminals, code repositories,
        # and cross-tool collaboration scenarios
        self.envs = environments 
    def run_rollout(self, max_steps=100):
        for env in self.envs:
            state = env.reset()
            for step in range(max_steps):
                # The model generates an action based on the current state,
                # such as producing code or a terminal command
                action = self.model.generate_action(state)
                # The environment executes the action and returns
                # a reward and the next state
                next_state, reward, done = env.execute(action)
                # Record the trajectory for later PPO/RLHF updates
                self.record_trajectory(state, action, reward)
                state = next_state
                if done:
                    break

3. Practical Testing in Agent Scenarios: CLI Environments and Long Context

Whether the path is an ultra-large new foundation model or extreme post-training, the result must ultimately be tested in real business scenarios. Terminal or command-line interface environments have become a key benchmark for measuring a model’s engineering capability. These scenarios require the model to understand not only natural language but also system commands, file system operations, process management, and other practical skills.

In such environments, two capabilities are especially important.

Context window support: Complex engineering tasks often involve reading large codebases and analyzing historical logs. Supporting long context windows of up to one million tokens has become a basic requirement. Long context tests not only the model’s memory but also its ability to extract key signals from large amounts of noisy information.

Tool calling and long-range planning: The model needs to carry out continuous action planning within a terminal environment. For example, when performing a security audit or code refactoring, the model must first probe the environment through commands and then dynamically adjust its strategy based on the results. This process demands strong logical rigor.

4. Engineering Guidance: How to Choose the Right LLM Base

For developers and enterprises facing models derived from different technical routes, how should they evaluate and deploy them effectively? The following dimensions are worth considering.

1. Assess task complexity and context length: If the business scenario mainly involves long document summarization or simple Q&A, a regular-scale model with long-context support may be sufficient. If the scenario involves cross-tool agent collaboration or complex code engineering, it is important to check whether the model has been deeply trained in long-horizon task environments during post-training.

2. Pay attention to post-training alignment quality: Do not rely solely on parameter count. A small or medium-sized model with high-quality post-training alignment can often match or even exceed a trillion-parameter model that has not been carefully tuned for a specific vertical domain.

3. Build a standardized evaluation benchmark: Before adopting a model, create a private test set based on your own business needs. Common intelligent programming tools or automated testing frameworks can be used to simulate real terminal environments and evaluate the model’s tool-calling success rate and multi-turn state retention.

4. Use mature cloud-native infrastructure: Deploying and serving large language models requires stable underlying compute support. Enterprises should take advantage of general-purpose container platforms, model inference services, monitoring, and alerting capabilities provided by cloud-native infrastructure to reduce operational burden. Object storage can be used to manage training data, and message queues can handle asynchronous agent task scheduling, helping to build a highly available LLM application architecture.

Conclusion

The evolution of large language models is not limited to rebuilding from scratch. Keeping the base model unchanged and using extreme post-training scaling to fully exploit its potential can also approach the upper limit of model intelligence. For developers, understanding the engineering logic behind different technical routes is more practical than blindly chasing parameter scale. In the coming wave of AI applications, those who can deeply integrate model capabilities with specific business environments will be better positioned to lead.