Maximizing a Fixed Foundation Model vs. Building a Trillion-Parameter Base: Two Competing Strategies in the LLM Race

The recent cadence of large language model releases has been relentless—new versions drop overnight, followed by successors before the sun rises. Beyond individual product names, the real story lies in two fundamentally different technical strategies emerging simultaneously.

One strategy “keeps the base”: the foundation model stays untouched while all engineering effort flows into post-training, pushing existing potential to its limits through extreme post-training scaling. The other strategy “starts over”: teams invest heavily in building a trillion-parameter-scale new foundation, raising the starting line at the pre-training level. Both pathways prioritize coding and agent capabilities, and both have embraced open-source.

The former is like adding floors on an existing foundation; the latter is pouring a deeper one. The collision of these two approaches in the same window offers the most compelling cross-section of today’s LLM competition: just how high can the post-training ceiling really go?

1. “Deepening” Rather Than “Replacing”: Understanding Where the Update Happens

Take a recent version update from the “keep the base” camp as an example. According to publicly released information, the foundation model was untouched compared to the prior release. All engineering effort went into post-training, summarized by the team as three levers:

1. Dozens of times more long-horizon task environments: letting the model “soak” in real engineering task chains for far longer;
2. Richer environment diversity: spanning terminals, code repositories, cross-tool collaboration, and other scenarios;
3. Dramatically extended post-training duration: simply stacking more compute into the training run.

The claimed capability gains focus on four areas: stronger coding ability, stronger cybersecurity ability, a refined post-training scaling methodology, and a continued commitment to open-source. The first three are capabilities; the last is a stance—when many teams are locking down weights tighter than ever, open release is itself a product strategy.

It is critical to understand: this type of update is not a “generational change.” It does not touch pre-training parameters or data. It is a targeted excavation toward the model’s “intelligence ceiling.” Grasping this distinction is the key to understanding where the entire strategy wins or loses.

2. The Three Levers and What Problems They Solve

2.1 Long-Horizon Task Environments: Shifting Training Distribution from Short Problems to Real Engineering Flows

In conventional post-training, models mostly face single-turn, short-chain tasks—a math problem, a code completion. Real engineering tasks are long-horizon: provision an environment, read an error, search documentation, edit configs, run verification, iterate based on results—a chain that easily runs dozens of steps, where any single failure can invalidate the whole sequence.

Scaling long-horizon environments by orders of magnitude is essentially pushing the reinforcement-learning training distribution toward authentic engineering workflows. The following abstract sketch illustrates the core difference between long-horizon environments and standard fine-tuning:

# Abstract sketch of a long-horizon task environment: one episode consists of multiple tool-interaction steps
episode = env.reset(task="Fix failing test cases in the repository")
for step in range(max_steps):
    obs = env.observe()        # terminal output / file contents / error messages
    action = policy(obs)       # model decision: read code, edit files, run commands
    env.step(action)          # most intermediate steps carry no immediate reward
    if env.finished:
        outcome = env.verify()             # final verification only at end of chain
        assign_credit(episode, outcome)    # long-horizon credit assignment
        break

Two direct benefits follow. First, the model is forced to learn decision-making under delayed feedback—the value of a current step is only verified dozens of steps later. Second, failure modes invisible in short tasks—mid-stream goal forgetting, drifting API call formats, state loss between steps—only surface when chains are long enough, and only then can they be addressed.

2.2 Environment Diversity: Guarding Against Overfitting to a Single Scenario

Long horizons without diversity risk degenerating into “grinding a specific environment.” Covering terminals, code repositories, cross-tool collaboration, and similar scenarios prevents the policy from converging on a single fixed interaction pattern.

From a generalization standpoint, environment diversity is the breadth of the training distribution: the wider the coverage, the more stable the model behaves when facing unseen tool combinations or unfamiliar error patterns. This is especially critical for agent use cases—real-world agent deployments vary wildly, and the narrower the training environments, the higher the probability of failure in production.

2.3 Post-Training Duration: The Simple Logic of Trading Compute for Quality

The third lever looks the most straightforward—just run post-training longer and throw more compute at it. But there is a serious consideration behind it. The reinforcement-learning phase suffers from sample-efficiency issues, especially in environment-interactive tasks where every useful trajectory requires real execution and trial-and-error, costing far more than standard supervised fine-tuning. Insufficient training time leaves the policy at “shallow correctness”; extended runs give the model a chance to move from “can get it right” to “consistently gets it right.”

Long-duration training usually accompanies a curriculum of progressive difficulty: converge on simple environments first, then gradually introduce longer and harder tasks, avoiding total collapse on high-difficulty work from the start. This mirrors an engineer’s career path—fix small bugs, then build modules, then lead a full project.

3. “Pre-Training Sets the Floor, Post-Training Sets the Ceiling”—A Balanced View

The industry once had an informal hierarchy: changing the base model was a “real upgrade,” while tweaking post-training was dismissed as “parameter tuning.” Recent practice is eroding that distinction—provided the base model still has untapped potential, post-training scaling can serve as a first-stage rocket.

A widely circulated formulation holds that pre-training determines the model’s floor, while post-training determines how high its ceiling can reach. This is roughly true in engineering terms, but two rigorous caveats are needed:

First, whether post-training “elicits” or “creates” capabilities remains debated in academia. One view holds that reinforcement learning merely excavates abilities already implicit in pre-training; another argues that in sufficiently rich and long-horizon environments, the RL search process can compose behavioral patterns never directly present in the pre-training data. Whichever explanation is closer to the truth, the engineering conclusion is the same: even with zero parameter changes, post-training can significantly lift real-world task performance.

Second, the post-training ceiling is indeed constrained by the foundation. If the base model lacks sufficient prior knowledge or reasoning patterns for a certain class of tasks, no amount of post-training can conjure them from nothing—which is precisely the rationale for the other strategy.

4. The Rebuild Path: When a New Base Becomes Necessary

In contrast to “deepening,” the rebuild path constructs a trillion-parameter-scale new foundation directly, typically using a sparse Mixture-of-Experts (MoE) architecture: enormous total parameter count, but only a small fraction activated per inference, balancing large capacity with controllable inference cost.

Common reasons to choose rebuilding include: the old base’s potential has been squeezed dry, post-training returns keep diminishing with marginal cost exceeding the cost of a fresh build; a new-generation base comes with comprehensive upgrades in pre-training data quality, mix ratios, and curriculum—benefits that can only be realized on a new foundation; and certain capabilities may be naturally absent from the old base’s priors, impossible to patch through post-training alone.

The costs are equally clear: pre-training is the industry’s most capital-intensive, most scale-dependent phase, measured in months, with risk measured in “starting over.” The battle between the two paths is, at its core, a bet on how much potential remains in the current foundation.

5. Side-by-Side Comparison

Dimension	Deepening Path (Base Unchanged)	Rebuild Path (New Base)
Primary Investment	Post-training environment construction & compute	Pre-training data, compute & architecture
Iteration Cycle	Weeks to months	Months to quarters
Cost Magnitude	Relatively lower	An order of magnitude higher
Capability Source	Eliciting and squeezing existing potential	Raising the prior starting point
Key Risks	Diminishing returns, environment overfitting	Scaling benefits falling short of expectations
Prerequisite	Base potential not yet exhausted	Base potential nearing its limit

Notably, the two paths are not mutually exclusive. A newly built foundation needs the proven post-training methodologies to translate raw potential into product-grade capability. In that sense, the methods refined by the deepening path become the “second-stage rocket” for the new base.

6. Open-Source: A Strategic Variable Beyond the Feature List

Both paths’ flagship releases have opted for open-source, and the significance for the developer ecosystem cannot be overstated:

• Verifiability: Open weights let the community independently reproduce benchmarks rather than relying solely on official leaderboards;
• Customizability: Enterprises can fine-tune and safety-align open weights for their domains, shaping the model to their needs;
• Cost Control: On-premises deployment avoids long-term per-token billing lock-in, critical for high-throughput or high-privacy scenarios.

In an era where many players guard their weights tightly, committing to open-source is itself a product strategy—the community feedback, ecosystem positioning, and developer mindshare it earns are assets closed-source models struggle to replicate.

7. Evaluation and Selection Guidance for Developers

Setting aside tribal loyalties, what developers and enterprises really need to know is how to pick the right model for their work amid a flood of releases. Four recommendations:

1. Test on real tasks, not benchmarks. Coding benchmarks tend to be short-horizon and closed-ended; real engineering is long-horizon and open. Run small-scale blind tests on your own codebase and agent workflows, watching for stability across multi-step tool calls.
2. Assess long-horizon capability, not point performance. Focus on goal tracking, error recovery, and contextual consistency—these are make-or-break factors for agent scenarios.
3. Evaluate the safety baseline. Whether the model has offensive/defensive cybersecurity testing capability and whether content-safety mechanisms are robust directly affect production readiness.
4. Run the deployment numbers. Open-source models enable on-premises deployment, but you must evaluate VRAM footprint, inference throughput, and ops cost. Closed APIs are convenient but carry long-term call-cost and data-boundary considerations. Teams can also leverage model services and inference infrastructure from providers such as Baidu AI Cloud to compare models side by side under controlled cost and run grayscale validation before committing.

Conclusion: The Possibility of Convergence

Back to the opening question: can squeezing every last drop from an unchanged foundation through extreme post-training beat a trillion-parameter new base?

In the short term, the cost-efficiency of post-training scaling is undeniable—it proves that when base potential remains, “deepening” can deliver a tangible intelligence leap at a full order of magnitude lower cost. In the long term, generational base-model transitions will inevitably arrive, and the post-training methodologies validated today will transfer directly to those new foundations, becoming the launchpad for the next leap.

In other words, the two paths are two sides of the same coin: rebuilding the base raises the starting line, and post-training determines the finish line. The takeaway for developers is straightforward—rather than obsessing over which “generation” a model’s base belongs to, focus on verifiable performance on your real tasks. After all, the model that reliably solves the problems in front of you is the right model.