The Debate on LLM Upgrades: Extreme Post-Training vs. Trillion-Parameter Base Models

I. Background: When Two Technological Paths Collide

Recently, the release pace of open-source large language models has accelerated significantly. New generations are hitting the headlines continuously, universally promoting coding and agentic capabilities as their core selling points. Observing these releases reveals an intriguing phenomenon: while all aim for a “massive capability leap,” they rely on two almost opposite technical paths.

Path A: Keep the Base Model, Push Post-Training to the Limit. The base model completely reuses the previous generation. All upgrades occur during the post-training phase—squeezing out the existing base model’s potential through orders-of-magnitude expansion in long-horizon task environments, richer environment types, and exponentially increased training compute.

Path B: Start Over, Build a Trillion-Parameter New Base Model. The parameter scale jumps by an order of magnitude. The model is rebuilt from pre-training, utilizing larger knowledge capacity and stronger capability priors to provide a higher starting point for subsequent alignment and reinforcement learning.

An invisible hierarchy exists in the industry: changing the base model is considered a “true upgrade,” while tweaking post-training is dismissed as “hyperparameter tuning.” Is this judgment valid? To answer this, we must dissect the technical logic of both paths.

II. Path A: How Post-Training Scaling Lifts the Intelligence Ceiling

1. A Commonly Misunderstood Premise

The premise for the post-training squeeze route is: the base model’s capability prior is far from fully activated. During pre-training, the model acquires general statistical patterns of language, code, and reasoning from massive corpora. However, these capabilities remain “latent”—the model knows how to do things but may not perform them correctly and consistently in real tasks. The goal of post-training is to translate this latent capability into stable, generalizable behavior.

An apt metaphor: pre-training determines the model’s floor (what knowledge and skills it possesses), while post-training dictates how high the ceiling it can reach (how much of its potential it can reliably unleash in complex tasks).

2. Three Key Levers

Based on public industry practices, extreme post-training typically revolves around three points:

• Massive Expansion of Long-Horizon Task Environments: Training the model by extensively immersing it in real-world engineering task chains. Long-horizon tasks require dozens or even hundreds of interactive steps to complete, such as locating a bug in a real code repository, modifying the implementation, passing tests, and handling regressions.
• Diversity of Environment Types: Covering terminal operations, code repositories, cross-tool collaborations, and various scenarios to prevent the model from overfitting on a single environmental distribution.
• Stacking Training Compute and Time: Reinforcement learning is fundamentally a “sample-evaluate-update” loop. More compute means more trajectory sampling, more accurate advantage estimation, and more stable policy convergence.

3. Why Now: Three Enablers of Post-Training Scaling

Post-Training Scaling is not a new concept. Its current role as a primary growth driver depends on three maturing conditions:

1. Prevalence of Verifiable Rewards: Whether code compiles, tests pass, or math results can be verified allows RL reward signals to rely on objective criteria rather than expensive and subjective human annotations.
2. Maturity of Agent Environment Infrastructure: Sandbox isolation, containerized execution, and standardized tool-calling protocols make large-scale parallel environment sampling engineering-feasible.
3. Marginal Trade-offs in Compute Allocation: As marginal returns from pre-training diminish, allocating incremental compute to post-training becomes a superior resource allocation strategy.

4. Anatomy of a Long-Horizon Task Environment

A typical verifiable long-horizon task environment consists of core elements: an initial state (e.g., a code repository snapshot), a task description (e.g., a real bug ticket), an action space (editing files, executing commands, calling tools), and an executable verifier (test suite). An illustrative example:

class LongHorizonTaskEnv:
    """Illustration of a long-horizon task env: multi-step, verifiable, cross-tool"""
    def __init__(self, repo_snapshot, test_suite):
        self.repo = repo_snapshot     # Initial state of the code repository
        self.tests = test_suite       # Executable verification suite
    def reset(self):
        return self.repo.checkout(), self.issue.text
    def step(self, action):
        # action: edit file / execute terminal command / call external tool
        obs = self.sandbox.execute(action)
        reward = 1.0 if self.tests.all_pass() else 0.0   # Sparse outcome reward
        done = self.tests.all_pass() or self.steps > MAX_STEPS
        return obs, reward, done

Designing the reward signal is challenging: pure outcome rewards (whether all tests pass) are sparse but reliable; process rewards (whether each step is sound) are dense but susceptible to reward hacking. In practice, verifiable outcome rewards dominate, supplemented by intra-group relative advantage estimation to reduce variance:

for batch in task_pool.sample_diverse(k=env_types):
    trajs = agent.rollout(policy_model, batch.env)   # Sample multiple trajectories for the same task
    rewards = [verifier.score(t) for t in trajs]     # Verifiable rewards
    adv = group_relative_advantage(rewards)          # Group baseline to reduce noise
    policy_model.update(trajs, adv)
    kl_anchor.step()                                 # Constrain policy drift to prevent capability forgetting

Additionally, two training pitfalls must be avoided. First, catastrophic forgetting: reinforcing one capability might harm general abilities, typically mitigated by KL constraints against a reference model. Second, reward speculation: the model learns shortcuts to inflate rewards without solving real problems, requiring the verifier itself to be strictly rigorous.

5. Where is the Ceiling for This Path?

Post-Training Scaling is not infinite. It fundamentally performs a search within the bounds of the base model’s capability prior: knowledge the base model hasn’t seen or skills it hasn’t acquired cannot be conjured from nothing. Once environment quantities and compute reach a certain level, marginal returns diminish—this is precisely why Path B exists.

III. Path B: The Logic and Cost of Trillion-Parameter Base Models

1. The Underlying Rationale from Scaling Laws

Widely validated scaling laws indicate that under the premise of matched growth in compute, data, and parameters, model loss decreases as a power law with scale. Capabilities like knowledge capacity and multi-step reasoning stability also exhibit qualitative leaps with scale. This means that to significantly raise the floor, the most direct method remains scaling up the base model.

2. Engineering Realities of Trillion Parameters

Ultra-large base models typically adopt a Mixture of Experts (MoE) architecture: the total parameter count is massive, but only a small fraction is activated per token, balancing capacity and inference cost. However, this introduces several engineering challenges:

• Parallel Strategy Combinations: Data parallelism, tensor parallelism, pipeline parallelism, and expert parallelism require coordinated tuning; communication overhead can significantly erode compute efficiency.
• Training Stability: The larger the scale, the more frequent loss spikes and gradient anomalies become, necessitating finer learning rate scheduling, gradient clipping, and real-time monitoring and alerting systems.
• Data Engineering: Quality cleaning, deduplication, and proportioning of tens of trillions of tokens directly determine the base model’s caliber. “Stacking parameters without sufficient data quality only amplifies defects.”
• Deployment Threshold: Even with sparse activation, serving and deploying these models requires significantly higher memory and cluster sizes than medium-sized models, raising inference costs and operational complexity.

3. Cost and Cycle

A new base model entails a heavy capital investment: training cycles measured in months, massive compute bills, and full-stack data and infrastructure coordination. Its benefits—a higher capability ceiling and a better starting point for subsequent post-training—only materialize if the strategic direction is accurate. If data proportioning or architectural design is flawed, the sunk costs are exceedingly high.

IV. Path Comparison: A Multi-Dimensional Decision Framework

Dimension	Path A: Extreme Post-Training	Path B: New Base Model Scaling
Capability Ceiling	Constrained by base model prior	Theoretically higher
Capability Floor	Inherits base model, stable and predictable	Elevates with scale but requires re-verification
Iteration Cycle	Short, rapid version updates	Long, measured in quarters
Marginal Cost	Relatively low, reuses base model investment	High, heavy pre-training investment
Inference Overhead	Basically unchanged	Depends on sparsity, usually increases
Primary Risk	Diminishing marginal returns	Training instability, flawed data proportioning

Based on this framework, three key conclusions can be drawn:

1. The two are not opposed; they are two ends of a spiral. A new base model provides a higher starting point for post-training; extreme post-training probes the boundaries of the base model’s potential, providing feedback for the next generation’s design. The typical cadence for mature teams is an alternating progression: “new base model -> post-training squeeze -> new base model.”
2. “Not changing the base model” does not mean “cutting corners.” When base model potential is unexhausted, post-training scaling is the most cost-effective primary driver. Allocating compute to post-training versus pre-training is fundamentally a trade-off of ROI, not a judgment of technical merit.
3. Parameter scale is no longer the sole narrative. With the maturation of sparse architectures, data quality, and post-training methods, “effective capability per unit of compute” reflects true competitiveness better than “total parameter count.”

V. How to Verify: Real-Feel Evaluation Beyond Benchmarks

The effectiveness debate between the two paths ultimately relies on evaluation. However, public benchmarks suffer from data contamination and overfitting to leaderboards; score differences do not necessarily equal experiential differences. For developers and enterprises, a more reliable approach is building custom evaluations:

1. Sample Real Tasks: Extract representative samples from your own code repository’s historical bug tickets, data pipeline tasks, and operations manuals.
2. Unified Execution Environment: Provide every candidate with the same sandbox, toolchain, and maximum interaction steps to control variables.
3. Verifiable Assessment: Use test pass rates, task completion rates, and manual spot-check quality scores as primary metrics, rather than model self-evaluation.
4. Concurrent Cost Accounting: Record token consumption and API overhead for task completion to formulate an “effectiveness per unit cost” metric, avoiding evaluations that look only at capability while ignoring cost.

# Enterprise custom evaluation illustration
tasks = load_internal_tasks(["repo_fix", "pipeline", "runbook"])
for candidate in candidates:
    results = [run_in_sandbox(candidate, t) for t in tasks]
    score = mean(r.pass_rate for r in results)     # Capability dimension
    cost  = mean(r.total_tokens for r in results)  # Overhead dimension
    report.add(candidate, score, cost)             # Comprehensive decision basis

VI. Implications for Developers and Enterprises

• Choose based on task experience, not benchmark scores. Running a blind test in your own real workflow is more convincing than any public leaderboard, especially when covering complex, long-horizon, multi-tool collaboration scenarios.
• Focus on total cost of ownership on the inference side. Even with sparse activation, trillion-parameter base models demand more deployment resources. Conversely, the extreme post-training route typically maintains constant inference overhead, making it friendlier for existing applications.
• Leverage cloud general capabilities to bridge engineering gaps. Whether building evaluation sandboxes or hosting agent applications, general compute and supporting services are indispensable. For instance, Baidu AI Cloud offers general product categories such as GPU compute, container platforms, object storage, log services, and monitoring/alerting. These can cover key phases like evaluation environment isolation, trajectory data storage, and training/inference observation, preventing teams from reinventing the wheel on infrastructure.
• Incorporate both paths into a single tech radar. Continuously tracking the industry’s shifting investment rhythm between post-training depth and base model scale helps anticipate the capability boundaries of next-generation models, allowing early planning for application architectures and cost models.

VII. Conclusion

“Extreme squeeze without changing the base model” and “a new trillion-parameter base model” represent two sides of the same coin in model iteration. The former approaches the ceiling within existing priors; the latter raises the ceiling itself. Scaling laws have not failed, and post-training scaling is far from exhausted. The real question is never which path is “more advanced,” but rather, under given constraints of time, compute, and capital, which path yields higher marginal returns. For the industry, this divergence is healthy: it repeatedly validates the division of labor where “pre-training sets the floor, post-training sets the ceiling,” ensuring that the substance of every upgrade is ultimately tested by real task experience.