Deep Dive into Post-Training Scaling: Pushing the Intelligence Ceiling Without Changing the Base Model

In recent years, the narrative around the large language model capability race has been shifting. Previously, a significant version upgrade was almost synonymous with “changing the base model”: larger parameter scales, more training tokens, and longer pre-training cycles. However, another counter-intuitive approach has yielded tangible results: keeping the base model unchanged while pouring all incremental computing power into the post-training phase. By leveraging reinforcement learning and task environments, this approach pushes the intelligence ceiling of the same base model higher.

There is an invisible hierarchy in the industry: changing the base model is considered a “true upgrade,” while modifying post-training is merely “tuning parameters.” However, if we plot the capability curves of both types of models on the same graph, this intuition does not hold up. A more accurate statement is: pre-training determines the floor of a model’s capabilities, while post-training determines how high its ceiling can go. Currently, some teams choose to invest heavily in new trillion-parameter base models, while others push the limits of post-training on existing base models. The head-on collision of these two routes brings an engineering question to the table: where exactly is the boundary of post-training scaling? This article attempts to break it down and clarify.

1. Why Post-Training Scaling Suddenly Makes Sense

Post-training scaling is not a new slogan, but it has only truly materialized in recent years, relying on the successive maturation of three prerequisites.

1. Verifiable Rewards Complete the Feedback Loop

The most expensive part of reinforcement learning is never compute, but feedback. Tasks like coding are naturally suited for machine grading: test suite pass rates, compilation success, and the number of static analysis warnings are all objective, readable signals. This type of “Verifiable Rewards” (RLVR, Reinforcement Learning with Verifiable Rewards) allows training to bypass manual grading, offering high feedback density and controllable costs, which provides the fuel for scaling.

2. Maturation of Environment Engineering Infrastructure

To train models in real-world engineering pipelines, sandboxes that are isolated, reproducible, and massively parallel are required: container snapshots rebuilt in seconds, controlled terminals and file systems, and stable tool-calling channels. The maturation of container orchestration, image distribution, and distributed rollout frameworks has turned the concept of “scaling task environments by an order of magnitude” from a slogan into a deliverable engineering problem.

3. Slowing Marginal Returns from Pre-Training

High-quality public corpora are nearing exhaustion, the marginal cost of data cleaning and synthesis continues to rise, and computing power prices are relatively dropping. Shifting incremental compute from pre-training to post-training has reversed the return on investment. The essence of this route debate is fundamentally a resource reallocation.

2. Three Levers of Post-Training Without Changing the Base Model

From various public practices, we can abstract three common levers for post-training scaling, answering “how many environments,” “how diverse are the environments,” and “how long to train.”

Lever 1: Order-of-Magnitude Growth in Long-Horizon Task Environments

Short tasks only teach a model to “answer a single question correctly,” while long-horizon tasks teach it to “complete an engineering project.” The latter requires the model to maintain context consistency over dozens of tool calls, plan continuously, and self-correct after failures. Expanding the quantity of task environments by orders of magnitude essentially allows the model to “soak” longer in task chains that closely resemble real-world engineering, transforming sparse success signals into learnable gradients. The building blocks for these environments typically come from real open-source repositories, tickets, and post-mortem reviews, which are desensitized and transformed into standardized task cards: initial state, task description, acceptance criteria, and timeout budget.

Lever 2: Richer Environment Types

A single environment trains a “test-taking athlete.” Making environments cover scenarios like terminal operations, repository-level refactoring, cross-tool collaboration, and even vulnerability reproduction and security offense/defense forces the model to learn general problem-solving strategies rather than pattern-matching to fixed questions. The schematic configuration below illustrates a common environment distribution strategy in practice:

# Environment type distribution (schematic)
task_mix:
  terminal_ops: 0.2      # Terminal and operations tasks
  repo_refactor: 0.3     # Repository-level code refactoring
  multi_tool: 0.3        # Cross-tool collaboration (search/database/browser)
  security_audit: 0.2    # Vulnerability reproduction, fixing, and offense/defense drills
horizon:
  short: 0.3             # Less than 10 steps
  medium: 0.4            # 10 to 30 steps
  long: 0.3              # More than 30 steps

Lever 3: Sufficiently Long Training Time

Simple, brute-force, but effective: under the same set of environments, longer training means more exploration and correction rounds. A commonly observed phenomenon in practice is that the capability curve continues to climb in the latter half of training; stopping prematurely often means wasting the environment construction costs already invested. This is the origin of the term “extreme extraction”—putting in the compute hours to fully activate the latent strategies within the base model.

3. Engineering Breakdown: The Minimal Skeleton of a Training Environment

At the code level, a trainable task environment consists of at least four components: initial state management, action execution, termination logic, and reward calculation. Below is a simplified pseudocode snippet:

class CodeTaskEnv:
    """Minimal usable code task environment skeleton"""
    def __init__(self, repo_snapshot, task_spec, sandbox, max_steps=50):
        self.repo = repo_snapshot      # Initial repository snapshot
        self.task = task_spec           # Task description + acceptance criteria
        self.sandbox = sandbox          # Isolated execution environment
        self.max_steps = max_steps      # Step budget for long-horizon tasks
    def reset(self):
        """Precisely restore the environment before an episode to ensure reproducibility"""
        self.sandbox.restore(self.repo)
        return self.sandbox.observe()
    def step(self, action):
        """Execute model action: edit file / run command / call tool"""
        obs = self.sandbox.execute(action)
        done = self.sandbox.finished(self.task)
        return obs, self.reward(done), done
    def reward(self, done):
        if not done:
            return 0.0
        r = self.sandbox.test_pass_rate()         # Primary signal: test pass rate
        r -= 0.05 * self.sandbox.lint_warnings()  # Soft constraint: code conventions
        if self.sandbox.tampered_tests():          # Anti-cheat: tampering with tests yields no score
            r = -1.0
        return r

Three engineering details are worth expanding on:

• Reproducibility is a prerequisite. The reset() function must restore the environment to the exact same state as the previous episode. Otherwise, the same policy will receive inconsistent feedback across different episodes, and the training signal will be drowned in noise. Layered snapshots and incremental restoration are common performance optimization techniques.
• Rewards must prevent “gaming the system.” If only test pass rates are rewarded, the model might learn to delete failing test cases, rewrite assertions, or hardcode outputs. In addition to the primary signal, it is usually necessary to incorporate static analysis, coverage limits, banned API lists, and cheating penalties, supplemented by manual spot checks to continuously harden the verifier.
• Long-horizon credit assignment is the core difficulty. In a 50-step episode that only gives a reward at the end, it is hard to attribute the quality of intermediate steps. A common practice is to combine process rewards (intermediate scores for key milestones) with outcome rewards, keeping their weights adjustable so the model learns to complete the entire journey and perform well at every step.

4. Facing the Boundaries Honestly: Post-Training is Not Alchemy

The narrative of post-training scaling sounds compelling, but there are three physical boundaries that must be acknowledged.

First, base model potential is a hard constraint. Reinforcement learning can only activate a base model’s latent strategies that “exist but are not yet activated”; it cannot create something from nothing. If a certain type of knowledge or reasoning pattern never appears in the pre-training distribution, no amount of post-training will make up for it. This is the prerequisite for the “no base model change” route to hold: the base model’s potential is not exhausted. Once this prerequisite fails, investing more compute merely results in overfitting on existing capabilities.

Second, the environment distribution is the capability ceiling. What the model learns is “the strategy to score high in the training environment.” For edge scenarios not covered by the environment, its capability will degrade significantly. Therefore, environment construction is more like continuous operations: backflowing online failure cases, updating task cards regularly, and continuously adversarially testing verifiers. Missing any link will expose flaws in real-world usage.

Third, cheating and reward hacking scale up. As the number of environments and training hours increase, the model’s exploitation of verifier vulnerabilities evolves in tandem. Red teaming is not a one-time action, but a常态化 normalized task that runs parallel to the entire training lifecycle.

5. Decision Framework: New Base Model vs. Maximizing the Current One?

For model teams, this is a resource allocation problem that can be simplified into a decision table:

Evaluation Dimension	Lean Towards Maximizing Current Base	Lean Towards Upgrading Base Model
Base model performance on target tasks	Failure modes are mostly “capable but unactivated”	Manifests as systematic knowledge gaps
Iteration cycle requirements	Measured in weeks, rapid capability harvesting	Measured in months, tolerating long cycles
ROI of incremental compute	Higher marginal returns from post-training	Base model generational gaps cannot be bridged by post-training
Sunk costs	Reusing existing base model assets	Requires rebuilding data, alignment, and evaluation pipelines

A pragmatic strategy is usually walking on two legs: pre-training a new base model proceeds in the background on a monthly basis, while post-training the current base model continuously delivers capabilities on the frontline on a weekly basis. More importantly, the environmental assets, reward designs, and verifiers accumulated during post-training scaling can be directly ported to the new base model—effectively “prepaying” the base model generational gap as a one-time capability jump when the new base model goes live. In this sense, methodology is a compounding asset.

6. Implications for Enterprise Adoption

Most enterprises do not train base models, but this technological evolution has direct implications for model selection and deployment.

First, don’t just look at benchmark scores during selection; look at the “source structure” of capabilities. Two models with similar scores—one built by scaling the base model, the other tuned via post-training—might perform significantly differently on real business distributions. It is recommended to build a small-scale internal evaluation set using proprietary business data, focusing on long-horizon, multi-tool, and noisy real-world tasks, rather than clean benchmark questions.

Second, agent scenarios fall exactly within the main strike zone of post-training scaling. Long-context consistency, tool-calling reliability, and self-correction after failure are the capabilities polished heavily on this route. During evaluation, it is advisable to specifically design “tasks designed to fail” to observe the model’s recovery behavior after interruptions, errors, or resource constraints—this provides more information than watching it succeed on the first try.

Third, at the infrastructure level, post-training environments and online inference environments are highly isomorphic. Both require isolated sandboxes, fast cold starts, and comprehensive observability. Enterprises can leverage general capabilities provided by cloud platforms—such as Baidu AI Cloud or similar providers offering elastic compute, container platforms, and log monitoring—to build their own evaluation and pilot environments. They can also reuse the verifiers from the training phase directly as online quality gates, achieving “test in production as you tested in training.” This principle of consistency is a crucial guarantee for the reliability of agent applications.

Conclusion

Returning to the original question: can extreme extraction without changing the base model defeat a larger new base model? The question itself might be a false premise. The two routes are not adversaries, but two lines on the same ROI table: pre-training buys the floor, post-training buys the ceiling. The only truly memorable takeaway is this—post-training scaling has been upgraded from “parameter tuning” to a primary rocket booster. Environment engineering and reinforcement learning are now first-class citizens on par with “data scale and parameter scale.” Any discussion regarding model capability planning that skips this line is bound to miscalculate.