Post-Training Scaling for LLMs: Approaching the Intelligence Ceiling Without Changing the Base Model

Introduction: The Second Battlefield

Over the past two years, the competition in large model capabilities has been almost synonymous with the competition in foundation model scale. More data, more parameters, more training compute—whoever had the most stood at the top of the leaderboards. But recently, the industry has split in an intriguing way: some teams choose to start over, pushing foundation parameters into the trillion range and trading an expensive pre-training generational leap for a higher intelligence ceiling. Other teams keep the foundation model unchanged entirely, betting all their resources on post-training—stacking reinforcement learning environments and compute to the extreme in order to “squeeze” the potential out of an existing base.

These two routes are colliding head-on, pushing an old question back to center stage: When a foundation model’s potential has not yet been exhausted, can scaled post-training investment substitute for a generational base model upgrade?

There is even an implicit hierarchy in the industry: changing the base model is considered a “real upgrade,” while doing post-training is dismissed as “just tuning.” But a growing body of practice is shaking that judgment. A more accurate summary is: Pre-training determines the lower bound; post-training determines how high the ceiling can be. This article focuses on the post-training scaling route, unpacking its underlying logic, key levers, and engineering practices, and offering a decision framework for route selection.

1. Underlying Logic: Pre-Training Sets the Floor, Post-Training Sets the Ceiling

To understand this statement, we need to return to the essential difference between the two types of training.

Pre-training: knowledge compression. Pre-training performs next-token prediction on massive corpora, compressing humanity’s accumulated knowledge and linguistic patterns into parameters. It determines what the model has “seen.” Knowledge coverage breadth, language ability, and the raw material for general reasoning are all shaped at this stage. In engineering terms, pre-training establishes the model’s capacity and representation quality—the lower bound for all subsequent capabilities.

Post-training: capability activation. Post-training (typically including supervised fine-tuning, or SFT, and reinforcement learning based on human feedback or verifiable rewards) does not significantly inject new knowledge. Its role is to activate capabilities the base model “potentially can do” into behaviors it “consistently demonstrates.” The same base model, after different quality levels of post-training, can perform vastly differently on real tasks.

There is a key concept here: the activation gap. Between what a foundation model can theoretically achieve and what it demonstrates by default, there is a significant gap. The intuition behind post-training scaling is: as long as the activation process can be scaled—environments can be automatically generated, rewards can be automatically verified—then sustained compute investment in this direction also follows scaling laws and can continuously close this gap.

The division of labor among the three types of training can be simplified as:

• Pre-training: Compresses world knowledge, determines capacity and lower bound;
• Supervised fine-tuning: Aligns instruction format, establishes task paradigms;
• Reinforcement learning: Iterates through trial and error in environments, approaches the behavioral upper bound.

2. Three Key Levers: How to Execute Post-Training Scaling

Based on recent public industry practice, achieving genuine “scaling” effects in post-training comes down to three levers. All three are indispensable and together form the propulsion system that raises the intelligence ceiling.

2.1 Long-Horizon Task Environments: From “Answering Questions” to “Getting Things Done”

Traditional fine-tuning data consists mostly of short “single question, single answer” samples with task chains of only one or two steps. This type of data is effective in early alignment stages but quickly saturates—the model learns the format but not how to “get things done.”

Real-world engineering tasks are long-horizon: fixing a bug may require reading the code repository, reproducing the issue, locating the root cause, modifying multiple files, running tests, and then handling regressions. The entire process can span dozens to hundreds of interaction steps over several hours. The first lever of post-training scaling is upgrading the training environment from “short Q&A” to “long-horizon task chains,” and expanding the environment’s scale and coverage by an order of magnitude or more. The core benefits are:

• Extending effective trial-and-error length: The model experiences failures, corrections, and backtracking over longer trajectories, learning “process capability” rather than “answer memorization”;
• Providing denser process signals: Every tool call and every state change in a long trajectory can serve as material for reward shaping;
• Aligning training distribution with application distribution: Agent-based applications test exactly long-horizon task capabilities, and alignment between the two minimizes capability transfer loss.

Engineering challenges are equally prominent: credit assignment over long trajectories is difficult—which step’s decision ultimately deserves credit for the final success? Common industry approaches include process reward models, Monte Carlo return estimation, and trajectory chunking and stitching. All of these essentially answer the question: “Which step was done right?”

2.2 Environment Diversity: Preventing Overfitting, Buying Generalization

Length alone is not enough—environments must be diverse. If training environments cover only a single type, the model overfits to that one distribution and becomes a “leaderboard machine”—stellar benchmark scores but mediocre real-world engineering performance. The second lever is multi-dimensional expansion of environment types:

• Terminal environments: Executing commands, managing processes, handling builds and deployments in controlled sandboxes;
• Code repository environments: Facing engineering-level code, completing cross-file retrieval, understanding, and modification;
• Cross-tool collaboration environments: Simultaneously invoking retrieval, databases, external services, and other tools, testing scheduling and composition capabilities;
• Document and knowledge work environments: Reading, synthesizing, and structured output of long texts;
• Multi-turn human-AI collaboration environments: Handling ambiguous instructions, clarification, and confirmation—interaction intelligence.

The value of diversity lies in generalization: after covering a sufficiently broad task distribution, the model learns the meta-ability of “how to learn new tasks” rather than fixed patterns. In practice, environment sets are typically organized with curriculum scheduling—easy to hard, with type-based allocation and scheduling—first ensuring stable foundational skills, then progressively probing higher task complexity.

2.3 Compute and Time: Treat Post-Training as a Pre-Training-Grade Engineering Effort

The third lever sounds the simplest but is the most counterintuitive: treat post-training as infrastructure engineering on the scale of pre-training. In early understanding, post-training was just “last-mile fine-tuning” with a negligible compute share. But when environments are long enough and tasks hard enough, every reinforcement learning step requires the model to complete full reasoning and tool-calling trajectories, with sampling overhead far exceeding ordinary language modeling:

• Sampling throughput becomes the bottleneck: A single parameter update requires massive trajectory rollouts; inference cluster throughput directly determines training progress;
• Training cycles measured in weeks: Achieving statistically meaningful improvement on long-horizon environments often requires weeks of sustained high-load training;
• Infrastructure requirements spike: High-throughput inference serving, trajectory storage, reward verification services, and training monitoring and alerting—missing any one slows iteration.

A simplified post-training loop is shown below (illustrative pseudocode):

for iteration in range(num_epochs):
    # 1. Parallel sampling: batch rollout in the task environment pool
    trajectories = sample_policy(policy_model, env_pool, n=N)
    # 2. Reward computation: deterministic verification for verifiable tasks, reward model for open tasks
    rewards = [verify(t) if t.verifiable else reward_model(t)
               for t in trajectories]
    # 3. Policy update: use KL constraint to prevent drifting too far from the base (prevent capability collapse)
    update_policy(policy_model, trajectories, rewards, kl_penalty)
    # 4. Environment scheduling: curriculum-based adjustment of task difficulty and type mix
    env_pool.adjust_curriculum()

This loop contains two engineering critical points: first, reward reliability—reward hacking is the most common failure mode, and verifiable rewards (such as unit test pass/fail or actual execution results of commands in a sandbox) are currently the most robust choice; second, KL constraint tightness—too loose may cause catastrophic forgetting of general capabilities, too tight prevents learning new behaviors.

3. How to Choose a Route: A Decision Framework for Technical Decision-Makers

The two routes are not a matter of faith but the optimal solution to the same scaling problem at different stages.

Dimension	Base Model Upgrade Route	Post-Training Squeeze Route
Core action	Expand parameters, expand data, retrain	Expand environments, expand compute, long-cycle RL
Primary benefit	Knowledge capacity and foundational capability ceiling	Behavioral capability and task execution ceiling
Delivery risk	High (full pipeline requires re-validation)	Low (base model behavior compatible)
Cost magnitude	Extremely high	Medium-high
Applicable premise	Base model has become a clear bottleneck	Activation gap has not yet converged

Signals to continue squeezing post-training: Each round of environment expansion or compute addition still yields measurable gains; target capabilities are “activation-type” (code, agents, tool use) rather than “knowledge-type”; the team values iteration speed and compatibility more.

Signals that a base model upgrade is necessary: Evaluations show the model has “never seen” certain knowledge, and post-training cannot activate capabilities that do not exist; shortcomings appear in capabilities deeply rooted in pre-training distribution, such as multilingualism and multimodality; marginal returns from post-training approach zero, indicating the activation gap has converged and the ceiling has been reached.

The endgame is convergence: Base model upgrades solve the “raw material” problem; post-training solves the “activation and alignment” problem. The current divergence in industry routes is essentially different teams being at different stages. The final form of top-tier models will inevitably be a stronger base model plus more extreme post-training.

4. Practical Recommendations for Enterprise Adoption

For enterprise developers who do not train foundation models themselves but want to make good use of large models, this route debate has direct actionable implications.

Model selection: Look at tasks, not narratives. Do not be anchored by narratives like “trillion parameters” or “full generational replacement.” Evaluate using your own business’s real long-horizon tasks, focusing on agent capabilities, tool-calling stability, long-context consistency, and other dimensions directly relevant to production—not general leaderboard scores.

Fine-tuning: Apply the methodology to yourself. Building a few dozen long-horizon task environments close to your business is better than massive single-turn Q&A data; prioritize verifiable rewards—use test cases when available, use execution results when an execution environment exists; even small-scale fine-tuning must prevent capability forgetting, which can be addressed by mixing in general data or controlling update magnitude.

Infrastructure: Compute and engineering pipeline are prerequisites. Post-training infrastructure requirements center on three things: high-throughput inference sampling, elastically scalable training compute, and reliable storage and monitoring. Cloud providers generally offer GPU compute, container platforms, object storage, and log monitoring as general services that enterprises can combine as needed to avoid heavy initial investment in self-built clusters. For teams without training expertise, directly using model services and fine-tuning capabilities on a platform is also a common way to lower the barrier.

Conclusion

The greatest value of this route debate is not in determining a winner but in correcting a cognitive misconception in the industry: post-training is no longer a nice-to-have “tuning” step but a first-stage rocket on equal footing with pre-training. Pre-training determines the lower bound; post-training determines the upper bound. The base model solves “whether it can”; post-training solves “whether it will.” For practitioners, the more practical takeaway is: when evaluating a large model, look beyond parameter count to its post-training investment and methodology; when building your own model capabilities, first ask clearly whether the bottleneck is insufficient raw material or insufficient activation. Different answers point to completely different directions for resource allocation.