Post-Training Scaling vs. New Foundation Models: Two Pathways to LLM Capability Breakthroughs

As the LLM competition progresses, a critical fork in the road has emerged. On one side is the pathway of maximizing post-training without changing the foundation model. On the other is the pathway of rebuilding from scratch with a larger-scale foundation model to forcefully push capability boundaries. Both approaches have converged recently, targeting coding and AI Agent tasks as their primary battlegrounds. Rather than evaluating specific vendors, this article analyzes these two pathways from a technical methodology perspective, breaking down their engineering logic, capability boundaries, and applicable scenarios to provide a reusable evaluation framework for developers and technical decision-makers.

1. The Core Question: What Determines the Capability Ceiling?

The industry has long harbored a hidden bias: modifying the foundation model is considered a “true upgrade,” while modifying post-training is dismissed as mere “fine-tuning.” The underlying assumption here is that pre-training dictates everything, and post-training is just the icing on the cake.

However, reality is correcting this assumption. The data distribution and representational capacity a model learns during pre-training establish its capability floor. How high a model can ultimately reach in terms of its capability ceiling depends heavily on how tasks, environments, and compute are organized during the post-training phase. In other words, pre-training provides the “raw materials,” while post-training determines “how tall the building can be.”

This leads to two distinctly different strategies:

• Strategy A: Maximizing Post-Training. The foundation model remains untouched. Reinforcement learning, long-horizon task environments, multi-scenario coverage, and training duration are maxed out to approach the potential ceiling of the current base model.
• Strategy B: Rebuilding a Larger Foundation Model. The old base model is replaced with a larger parameter-scale model. Capabilities are redistributed from the pre-training phase, followed by post-training alignment.

The cost structures, risk curves, and return profiles of these two strategies are fundamentally different. Let’s explore them in detail.

2. Strategy A: Three Engineering Pillars of Post-Training Scaling

Post-training scaling is not simply “training for a few more epochs.” It is a systematic engineering effort that can be broken down into three mutually reinforcing pillars.

2.1 Long-Horizon Task Environments: Letting Models “Soak” in Real Task Chains

Short-horizon tasks (single-turn QA, single function generation) provide dense but shallow reinforcement signals. Long-horizon tasks (multi-file refactoring, cross-step debugging, dependency chain reasoning) provide sparse but deep reinforcement signals. The first pillar of post-training scaling is expanding the training environment from “single-point questions” to “task chains.”

Key engineering considerations include:

• Verifiability of task chains: Long-horizon tasks must be automatically evaluable for success or failure; otherwise, the reinforcement learning loop cannot close. A common approach is building repository-level tasks with test cases, using test pass rates as the reward signal.
• Intermediate state reward design: Long-horizon tasks shouldn’t only provide terminal rewards. Process rewards must be given for intermediate steps (e.g., locating the correct file, writing compilable code), or credit assignment becomes extremely difficult.
• Utilization of failure samples: Long-horizon tasks have high failure rates. The failure trajectories themselves serve as high-quality negative samples for preference optimization.

A schematic task environment configuration (pseudocode) looks like this:

task_env = {
    "task_type": "repo_level_patch",
    "max_steps": 40,              # Upper limit of allowed interaction steps
    "reward": {
        "unit_test_pass": 1.0,    # Terminal reward
        "compile_success": 0.2,   # Process reward
        "file_localization": 0.1  # Process reward
    },
    "rollout_per_task": 8         # Sampling multiple trajectories per task
}

The value of a long-horizon environment lies in forcing the model to learn the complete closed-loop of “plan-execute-verify-correct,” which is the core of AI Agent capabilities.

2.2 Environment Diversity: Covering Terminals, Repositories, and Tool Collaboration

Models trained in a single environment easily overfit to specific interaction patterns. Environment diversity ensures the model sees enough “tool interfaces” and “state spaces”:

• Terminal environments: Command-line operations, file system I/O, and process management test the model’s precise control over structured output.
• Code repository environments: Multi-file dependencies, version states, and build systems test cross-file reasoning.
• Cross-tool collaboration environments: Calling retrieval, computation, and external services tests tool selection and parameter filling.

The direct benefit of diversity is generalization: the model won’t collapse just because a tool’s API changes. The indirect benefit is robustness: when facing unseen but structurally similar tasks, the model can transfer existing interaction strategies.

2.3 Extended Post-Training Duration: Putting Compute in the Right Place

The third pillar is the most straightforward—extending post-training time and expanding rollout scale. However, “pouring in compute” must be done efficiently. Key considerations include:

• Sampling efficiency: Single trajectories in long-horizon tasks are time-consuming. Asynchronous rollouts and parallel environment pools are needed to increase throughput.
• Reward stability: The longer the training, the higher the risk of reward model drift. Periodic calibration or rule-based reward fallbacks are necessary.
• Catastrophic forgetting control: Prolonged reinforcement learning can cause the model to lose the general capabilities of its base model. A common practice is to mix in a certain proportion of general instruction data as regularization.

Together, these three points form the technical foundation for “raising the ceiling without changing the base model.” However, it is crucial to recognize that the returns on post-training scaling are diminishing. The closer the model gets to the base model’s potential ceiling, the smaller the improvement per unit of compute.

3. Strategy B: The Logic and Cost of the New Foundation Model Path

The logic behind rebuilding a larger-scale foundation model is straightforward: scaling parameter and data size simultaneously allows pre-training to learn richer representations. This raises the capability floor overall, meaning post-training only needs to fine-tune from a higher starting point.

This pathway carries three non-negligible costs:

1. Training cost and cycle: Large-scale pre-training requires an order-of-magnitude increase in compute, data engineering, and training stability. Cycles are measured in months.
2. Inference cost: Larger parameters mean higher inference overhead. Deployment cost pressures will传导 to product pricing and concurrency capabilities.
3. Post-training still needs redoing: A new base model does not equate to new capabilities. Coding and agent capabilities still rely on post-training alignment, effectively doubling the investment in “pre-training + post-training.”

Therefore, the new foundation model pathway is not a “shortcut” but rather a trade of “larger investment for a higher floor.” It is suited for scenarios that can absorb long-term training costs and have hard requirements for the capability floor.

4. Comparative Framework for the Two Pathways

Comparing the two pathways side-by-side provides a clearer perspective:

Dimension	Post-Training Scaling (No Base Model Change)	New Foundation Model Pathway
Capability Floor	Unchanged	Raised
Capability Ceiling	Approaches base model’s potential ceiling	The ceiling itself is higher
Training Cost	Medium, concentrated on post-training compute	High, double investment in pre + post-training
Inference Cost	Unchanged	Typically increases
Risk Points	Diminishing returns, forgetting, reward drift	Training instability, long cycle, high cost
Applicable Scenarios	Base model potential untapped, fast iteration pace	Capability floor insufficient, long-term investment viable

A practical rule of thumb: If the current base model’s failure mode on target tasks is primarily “knows how to do it but is unstable,” post-training scaling is more cost-effective. If the failure mode is “fundamentally doesn’t know how,” it indicates insufficient representational capacity, requiring a new foundation model.

5. The Specifics of Coding and Agent Scenarios

Why do both pathways target coding and agents as their main battleground? Because these two task types simultaneously test both the “floor” and the “ceiling”:

• Coding tasks: Syntax and API knowledge belong to the floor, covered by pre-training. Complex refactoring, cross-file reasoning, and debugging strategies belong to the ceiling, reinforced by post-training. Both are indispensable.
• Agent tasks: Correct tool calling format belongs to the floor. Multi-step planning, error recovery, and resource trade-offs belong to the ceiling. Long-horizon environment training significantly improves the latter.

This also explains why “coding performance” has become a frequent marketing metric—it simultaneously reflects knowledge coverage and interaction strategy, serving as a comprehensive manifestation of both pathways’ capabilities.

6. Practical Recommendations for Developers

1. Differentiate failure modes when evaluating models: Conduct error attribution first. Distinguish between “knowledge deficit” and “unstable strategy” before deciding whether to switch models or add post-training.
2. Build your own long-horizon evaluation set: Single-turn benchmarks cannot reflect agent capabilities. Build repository-level task sets with test cases, evaluating using both pass rate and step efficiency metrics.
3. Focus on inference cost, not just capabilities: The new foundation model pathway offers a high capability floor, but inference overhead may impact product viability. End-to-end cost accounting is necessary.
4. Post-training is not a panacea: When a model systematically “doesn’t know how” to perform a target task, continuing to pile on post-training compute is inefficient. Switch the base model decisively.

7. Conclusion

“Maximizing post-training” and “rebuilding a new foundation model” are not mutually exclusive; they are two curves with different slopes on the same capability coordinate system. The former is highly cost-effective when the base model’s potential is untapped, while the latter is irreplaceable when the capability floor is insufficient. For developers, the key is not taking sides, but establishing an analytical framework of “failure mode attribution—pathway selection—cost accounting.” Ultimately, the endgame of the model competition is not a contest of parameter scale, but rather who can deliver capabilities reliably into real task chains with more reasonable engineering investments.