LLM Iteration Strategy Debate: Deep Post-Training Scaling vs. Retraining the Base Model

Over the past two years, the release cadence of open-source large models has visibly accelerated, with new versions making the rounds every few weeks. But lay the technical reports side by side and a more interesting fact emerges: what looks like a race of version numbers is really a fork in engineering philosophy—

One path chooses “keep the base fixed, dig deep into post-training”: the pre-trained model stays unchanged, and all compute and engineering effort is poured into the reinforcement learning stage, squeezing the upper bound of intelligence out bit by bit through environment scaling. The other path chooses to “tear it down and start over”: directly retrain a new base model with a leap in parameter scale (trillion-level), rebuilding the capability foundation at the data and architecture level.

The collision of these two paths is exactly the most worthwhile question in large-model engineering today: given that the base model’s potential has not been exhausted, how far can post-training scaling push a model? And at what critical point will a new base model open up a generational gap? This article attempts to take this question apart.

1. First, Clarify: What Pre-Training and Post-Training Each Determine

To compare the two paths, you first need to understand the internal division of labor in large-model capabilities:

• Pre-training determines the lower bound. Self-supervised learning on massive corpora determines the model’s knowledge reserves, language ability, and the “chassis” of its world model. Knowledge the base model has never seen can hardly be conjured up from nothing in later stages.
• Post-training determines the ceiling. SFT, RLHF, reinforcement learning based on verifiable rewards, and similar stages are responsible for turning “knowing” into “doing”: behavioral norms, tool invocation, and long-horizon task execution are all shaped at this stage.

An implicit hierarchy of contempt exists in the industry: changing the base model counts as a “real upgrade,” while changing post-training is dismissively called “tuning parameters.” This view deserves correction. If model capability is seen as an interval [lower bound, upper bound], pre-training raises the entire interval, while post-training determines which point in that interval the model can actually reach. When the base model’s potential has not yet been fully released, post-training scaling can absolutely serve as the “first-stage rocket,” and its marginal returns are not necessarily lower than retraining the base.

2. Path One: No New Base, How to “Squeeze to the Limit” in Post-Training

The core assumption of this path is: the existing base model’s knowledge and representational capacity are already sufficient, and the bottleneck is at the behavioral layer—the model doesn’t “not know how,” it just “hasn’t been taught to organize its capabilities into long-horizon, reliable task execution.” Therefore its three levers all revolve around the reinforcement learning environment:

1. Scaling up long-horizon task environments. Let the model “soak” longer and deeper in real engineering task chains. This is crucial for agent capabilities: suppose single-step decision accuracy is 99%; the probability that a 100-step task chain is entirely correct is 0.99 to the 100th power, about 36.6%. Long-horizon reliability cannot be stacked up from single-point capability; it can only be trained by repeatedly working in long-chain environments, letting the model learn mid-course error correction, state management, and cross-step consistency.

2. Diversifying environment types. Cover scenarios such as terminal operations, repository-level code modification, and cross-tool collaboration. Environments are essentially the “curriculum” of reinforcement learning: a policy trained in a single environment easily overfits to a fixed template, while diverse environments force the model to learn transferable, general problem-solving patterns rather than memorizing the “walkthrough” of one particular environment.

3. Super-long stacking of post-training time and compute. Simple and crude, but reinforcement learning also exhibits scaling behavior: more environment interactions and more training steps mean more trial-and-error samples and finer policy polishing.

A schematic pseudocode snippet can describe the core loop of this stage:

# Post-training "environment-trajectory-reward" loop (schematic)
for round in range(total_rounds):
    task  = env.sample_task()          # fix bug / write module / multi-tool collaboration / attack-defense drill
    state = env.reset(task)
    traj  = []
    while not env.done(state):
        action = policy(state, history=traj)
        state  = env.step(action)      # real execution: terminal commands, compilation, tests, tool returns
        traj.append((state, action))
    reward = env.verify(traj)          # verifiable reward: unit tests pass? build succeeds? constraints satisfied?
    rl_optimizer.update(policy, traj, reward)

The reason this path first gained traction in coding and agent scenarios is that rewards in these domains are naturally “verifiable”: whether unit tests pass or fail, whether a build succeeds, are objective signals requiring no human labeling, which makes large-scale automated RL possible. Security attack-defense scenarios are similar—task success or failure has a clear determination. In a sense, “verifiability” determines where the sweet spot of post-training scaling lies.

Of course, the boundaries of this path are also clear:

• Reward hacking risk: the model may learn to cater to the reward function rather than truly solve the problem, requiring continuous red-team auditing and reward design iteration;
• Environment overfitting: scores are high in the training environment but degrade when the environment template changes, so evaluation must retain out-of-distribution held-out tasks;
• Knowledge blind-spot ceiling: knowledge the base model doesn’t have cannot be squeezed out even by the strongest reinforcement learning.

3. Path Two: Tear It Down and Start Over, Retraining a Trillion-Level New Base

The other path judges the opposite: rather than repeatedly squeezing the old base, rebuild from the data recipe, architecture, and parameter scale. Typical moves include: updating the pre-training data mix (new corpora, high-quality synthetic data, curriculum-style data), upgrading the architecture (such as sparse mixture-of-experts structures), pushing parameter count to the trillion level, and simultaneously extending “chassis capabilities” such as long context.

The benefits are equally clear:

• The lower bound is raised overall. Broader knowledge coverage and stronger representational capacity directly reduce “don’t know” type errors;
• Unlocks capabilities post-training cannot fill in. New knowledge, longer context, and stronger out-of-distribution generalization are “chassis generational gaps”; post-training can only approach them, not cross them;
• Compounds with post-training. Once the new base goes live, the previous environment and reward systems can be migrated over wholesale, raising the training starting point.

The cost lies in: pre-training compute investment is usually one to two orders of magnitude higher than post-training; data engineering and training stability risks are significant; the iteration cycle is measured in months, far slower than post-training’s weeks; and a new base often requires rerunning the full alignment and safety evaluation pipeline.

4. Comparing the Two Paths from a Decision Perspective

Dimension	Deep post-training	Retraining a new base
Iteration cycle	Weeks	Months
Per-round investment	Relatively low	One to two orders of magnitude higher
Capability ceiling	Constrained by base potential	Can achieve generational leaps
Main risks	Reward hacking, environment overfitting	Training instability, uncertain ROI
Core assets	Reusable environments and reward systems	Data recipes and architecture experience

Worth emphasizing: post-training investment is not a one-time expenditure. Environment construction, reward design, and evaluation benchmarks are all engineering assets that can be reused across base models—equivalent to prepaying “compound interest” for the next base upgrade.

5. A Practical Judgment Framework: Is Your Base “Squeezed Dry”?

For engineering teams, the real question is: when should you keep squeezing post-training, and when should you consider a base iteration? Here is a judgment method based on error attribution.

Step one: sample from failure cases and manually classify errors into two types:

• “Don’t know” type: lacking knowledge, lacking concepts, hallucinating domain facts—this is a pre-training gap;
• “Knows but does it wrong” type: chaotic planning, tool invocation mistakes, long-horizon execution going off track—this is a behavioral-layer gap, the sweet spot of post-training RL.

A schematic logic for reference:

if "behavioral gap" share of errors is high (e.g., > 70%):
    prioritize additional post-training investment: expand environments, lengthen horizons, stack compute
elif errors concentrate in "knowledge gap" and hit core business domains:
    consider continued pre-training (data supplementation) or plan a base model replacement
else:
    maintain the current cadence, keep doing error-attribution monitoring

At the same time, watch two signals: one is evaluation saturation—improvements on out-of-distribution held-out tasks continue to narrow, indicating the behavioral-layer dividend is peaking; the other is whether the gap between hard base-model capability metrics (long context, knowledge breadth) and business requirements is widening.

At the engineering implementation level, large-scale reinforcement learning has specific infrastructure requirements: rollout (environment interaction trajectory generation) needs high-throughput inference clusters, trajectories and checkpoints need large-capacity object storage, and long-cycle training depends on a well-developed logging and monitoring alerting system. Teams can build this pipeline on cloud GPU compute, container platforms, storage, and general monitoring services—for example, using the general-purpose compute and development infrastructure provided by Baidu AI Cloud to carry mixed training and inference workloads. The engineering focus is ensuring rollout throughput matches training throughput, avoiding GPUs idling while waiting for environment returns.

6. Conclusion: Not Either-Or, but a Rhythm

Put both paths back into a real engineering context, and they look more like a two-stage rocket that ignites alternately: the base model gets a major version upgrade every longer cycle, and the gaps in between are continuously filled by post-training at a minor-version cadence. Pre-training determines the lower bound, post-training determines the ceiling you can reach; when the base model’s potential is not yet exhausted, post-training scaling fully deserves the title of “first-stage rocket”; and when error attribution shows that the knowledge gap has become the principal contradiction, it is time to plan a base model replacement.

For most teams, rather than agonizing over “which path is stronger,” the more practical question is how to build your own error-attribution system and reusable environment assets—the former determines your judgment in switching between the two paths, and the latter determines the cost of each switch.