Post-Training Scaling Limits and the New Generation of Large Base Models

1. Background: Two Technical Roadmaps Collide

As the large model race entered the second half of 2025, an intriguing pattern emerged across the industry. On one side, a new generation of base models continued to push parameter counts to record levels. On the other, some teams stuck with their existing base models and relied entirely on post-training to extract more intelligence from the same foundation. Both approaches launched within roughly the same time window, targeted overlapping capabilities—coding proficiency and agent tasks—and released open-weight models.

This creates a technically valuable point of comparison: How far can post-training scaling go without switching base models? And does a from-scratch, next-generation mega base model deliver a true generational leap?

This article does not review or recommend any specific product. Instead, it abstracts these two approaches into general technical questions and examines their principles, costs, applicable scenarios, and engineering implementation.

2. Clarifying the Concept: What Is Post-Training Scaling

Large model training typically consists of two stages:

• Pre-training: Next-token prediction on massive general-purpose corpora. This determines the model’s knowledge breadth and its baseline capabilities.
• Post-training: Building on the pre-trained base model through supervised fine-tuning, preference alignment, and reinforcement learning to turn a model that “can talk” into one that “can work.”

The conventional view treats pre-training as the “real upgrade” and post-training as mere “parameter tuning.” However, recent practice shows that when a base model’s potential has not been fully unlocked, reinforcement learning scaling during post-training can become an independent engine of performance growth.

Three key levers of post-training scaling:

1. Long-horizon task environments: Letting the model interact continuously within realistic, multi-step engineering task chains—for example, a complete loop from requirements understanding to code modification to test verification.
2. Environment diversity: Covering terminal operations, code repository management, cross-tool collaboration, and other scenarios to prevent the model from overfitting to a single distribution.
3. Post-training time and compute investment: Extending reinforcement learning training steps and expanding sampling scale so that policy optimization becomes more thorough.

In short: Pre-training determines the model’s floor; post-training determines how high its ceiling can reach.

3. Approach A: Squeezing the Existing Base Model to Its Limits

3.1 Core Logic

This approach assumes that the existing base model’s capabilities have not been fully tapped. Teams therefore direct all incremental compute into post-training, using aggressive reinforcement learning scaling to raise the intelligence ceiling.

Specific indicators include:

• Significant coding improvements in internal evaluations, suggesting real progress on code generation, debugging, and refactoring tasks.
• Leading results on agent benchmarks in terminal or command-line environments. These benchmarks share a common trait: the model must autonomously plan, execute commands, observe results, and correct errors within a CLI environment.

3.2 Why Terminal Agent Scenarios Are a Critical Testing Ground

Agent tasks in terminal environments differ fundamentally from ordinary conversational tasks:

• Large state space: File systems, processes, environment variables, and network states can all change.
• Delayed and sparse feedback: After executing a command, multiple steps may be needed before the final result becomes visible.
• Irreversible errors: Deleting files by mistake or applying incorrect configurations can cause total task failure.

Strong performance on terminal agent benchmarks therefore indicates solid long-horizon planning, tool invocation, and self-correction capabilities—precisely the core requirements of current intelligent coding tools.

3.3 Costs and Limitations

This approach carries clear costs:

• The base model’s ceiling is a hard constraint. If the foundation has structural deficiencies in a particular capability, post-training can rarely compensate fully.
• Training costs are deferred, not eliminated. Environment interaction and sampling during reinforcement learning still consume substantial compute—this is not a free lunch.
• Environment construction is difficult. High-quality long-horizon task environments require significant engineering investment and are hard to reuse.

4. Approach B: The New Generation of Ultra-Large Base Models

4.1 Core Logic

The alternative route starts from scratch, building new base models with parameter scales reaching a trillion or more. Its advantages include:

• Greater knowledge capacity and generalization: Larger parameter counts typically mean stronger pattern fitting and knowledge storage.
• More natural unified multi-task modeling: Coding, reasoning, multilingual capabilities, and other skills can develop more evenly within a single base model.
• Larger headroom for iteration: A new base model leaves more room for subsequent post-training and fine-tuning.

4.2 Costs and Challenges

• Extremely high training costs: Pre-training trillion-parameter models requires large-scale compute clusters and extended training time.
• Higher inference costs: Larger parameters generally mean higher memory usage and latency during inference, placing greater demands on deployment.
• Harder post-training: Larger base models are often more difficult to align and tune through reinforcement learning.

5. Comparison Framework

Dimension	Keep Base Model + Post-Training Scaling	New Ultra-Large Base Model
Core investment	RL environments and training steps	Pre-training compute and data
Source of capability gains	Policy optimization, task generalization	Knowledge capacity, pattern fitting
Iteration cycle	Shorter, faster iteration	Longer, requires retraining
Inference cost	Relatively manageable	Typically higher
Risk	Hard to break through base model bottlenecks	High cost of training failure
Best suited for	Unexhausted base model potential, focused tasks	Pursuing broad capability ceilings

6. Engineering Considerations: How to Evaluate and Choose

For developers and enterprise users, choosing between these approaches depends on specific needs:

1. Task focus: If the core requirement is coding and terminal agents, the post-training scaling route may deliver results faster.
2. Inference cost sensitivity: If the deployment environment is sensitive to latency and memory, smaller models have an advantage.
3. Long-term iteration expectations: If continuous capability improvement matters, pay attention to the team’s release cadence and open-source strategy.
4. Benchmark alignment: Agent benchmarks in terminal or CLI environments more closely reflect real-world usage of intelligent coding tools and deserve close attention.

6.1 A Typical Agent Task Loop in Pseudocode

# Pseudocode: a typical terminal agent loop
while not task_done:
    observation = get_terminal_state()
    plan = model.plan(observation, task_goal)
    command = model.generate_command(plan)
    result = execute_in_sandbox(command)
    if result.error:
        model.reflect_and_retry(result.error)
    task_done = check_goal(result)

This loop requires the model to understand state, plan, generate commands, reflect on errors, and retry. The post-training scaling approach focuses its reinforcement efforts specifically on these capabilities.

7. Conclusion: Not Either/Or, but Two Levers Working Together

Squeezing an existing base model to its limits and building a next-generation ultra-large base model are not mutually exclusive. They represent two different levers for improving model capabilities:

• The pre-training lever: Determines the capability floor and knowledge breadth.
• The post-training lever: Determines the capability ceiling and task specialization.

When a base model’s potential remains unexhausted, post-training scaling is the more cost-effective choice. When the base model becomes the bottleneck, a new generation of base models becomes the necessary leap. For developers, understanding the principles and boundaries of these two routes is more valuable than simply comparing parameter counts or leaderboard rankings.

What deserves attention going forward is whether the two routes can combine forces: using a new base model to provide a higher starting point, then applying aggressive post-training scaling to push the ceiling even further. That is the sustainable path for the continued evolution of large model capabilities.