Large-Scale Post-Training in Practice: How Trillion-Parameter Models Build Long-Horizon Agent Capabilities

1. Post-Training: The Main Arena for Capability Breakthroughs

Scaling laws still hold during pre-training, but high-quality data is becoming increasingly scarce and marginal returns continue to decline. The industry consensus is shifting: apply post-training compute that is several times—or even orders of magnitude—greater than what was used for the base model to unlock capability breakthroughs. There is a well-known saying that the only way to discover the limits of the possible is to venture a small step beyond them into the impossible. This applies remarkably well to post-training: a model’s capability boundary is often redefined precisely when it is pushed one step further toward harder tasks.

Post-training typically consists of supervised fine-tuning (SFT) and reinforcement learning (RL) based on human feedback or verifiable rewards. The unique value of RL is that the model no longer merely “imitates demonstrations.” Instead, it learns to “get things done” through interaction with an environment, trial and error, and receiving rewards or penalties. In domains such as coding and mathematics, where reward signals are naturally verifiable—compilation passing or test cases passing means correct—RL delivers the most significant gains.

However, large-scale RL has an engineering prerequisite that is easy to underestimate: compute investment grows linearly, while capability returns depend on the efficiency of the entire technology stack. A single RL training run involves thousands or even tens of thousands of trajectory samples. Inefficiency in any layer—sampling, inference, environment execution, or reward computation—gets amplified multiplicatively. As a result, mature teams systematically optimize every layer of the stack rather than simply adding more GPUs.

Another key decision is “joint training”: mixing hundreds of downstream domains in a single training program rather than fine-tuning each domain sequentially. The latter approach easily leads to a “capability seesaw”—improving coding while degrading mathematical reasoning. Joint training, combined with curriculum scheduling and data mixture control, is required to achieve stable gains across all domains simultaneously.

2. Full-Stack Engineering Optimizations for Large-Scale RL

1. RL training framework. The core challenge is balancing throughput and latency. Architectures that decouple decision-making from learning and employ asynchronous sampling prevent compute from idling between “generating trajectories” and “updating parameters.” At the algorithm level, policy gradient methods such as proximal policy optimization (PPO) and its variants, combined with techniques like rejection sampling, help stabilize the training curve.

2. Long-context inference. Sampled trajectory lengths follow a long-tail distribution, and a small number of extremely long samples can drag down overall throughput. Common industry practices include prefix caching and KV cache reuse, paged memory management, and attention variants optimized for long sequences. The goal is to maintain acceptable decoding speed even for contexts reaching millions of tokens.

3. Training efficiency. Hybrid parallelism strategies—combinations of data, tensor, pipeline, and expert parallelism—along with operator fusion, communication-computation overlap, mixed precision, and selective recomputation, are necessary to convert every increment of new compute into effective parameter updates.

4. Sandbox infrastructure. Agent-type tasks require models to act in “real environments”: executing terminal commands, reading and writing files, and calling external tools. A sandbox must simultaneously satisfy isolation (failed tasks do not contaminate the host), reproducibility (same task, same result), and resettability (snapshot rollback in seconds), while supporting large-scale parallel scaling. The stability of this layer directly determines the quality of RL data.

5. Scheduling system. RL training is inherently a heterogeneous workload: training clusters and inference clusters have different resource characteristics. Task priority, preemption, fault tolerance, and elastic scaling all require a unified scheduling layer for orchestration. Otherwise, compute utilization gets wasted in the “waiting” at each stage.

6. Environment quality inspection platform. This is the most easily overlooked layer, yet it determines the upper bound of RL performance. An unsolvable task sends noise signals to the model, while a task with exploitable loopholes induces reward hacking. A shared quality inspection platform validates tasks before they enter training: Is the task solvable? Are the tests valid? Does the reward provide meaningful differentiation? Garbage in, garbage out—environment quality is the ceiling for RL effectiveness.

3. Million-Token Context: From “Readable” to “Usable”

Context windows at the million-token scale are shifting the feasibility boundary for three categories of scenarios:

• Whole-repository code understanding: A medium-sized repository can be “read through” without chunked retrieval. Cross-file dependencies and implicit conventions all enter the model’s field of view. This is especially critical for long-horizon agents—task execution no longer depends on fragmented context constrained by retrieval quality.
• Long-form research: Cross-referencing and multi-hop reasoning across financial reports, legal documents, and scientific literature.
• Persistent agent memory: State and decision rationale across multiple tool-call rounds can be fully preserved in context, reducing repetitive exploration caused by “memory loss.”

From an engineering perspective, ultra-long contexts require positional encoding extrapolation, careful trade-offs between memory and speed, and scheduling strategies that work with prefix caching. It is worth noting that long context and retrieval augmentation are not substitutes for each other: the former provides a “complete view,” while the latter addresses “cost control.” Production systems typically use a hybrid of both. For evaluation, the simplistic “needle in a haystack” test is no longer sufficient to characterize capability. Tasks that more closely resemble real workloads—multi-hop question answering, global aggregation, and long-range consistency—are what matter.

4. Long-Horizon Agent Capabilities: What to Evaluate and How

For evaluating long-horizon agents, the industry is converging on two dimensions:

First, end tasks in real environments. These benchmarks place models in resource-constrained real terminal environments, with each task equipped with strict dedicated tests to assess whether the model can “get things done under real constraints.” Public evaluations show that pass rates for leading models have generally entered the 60%+ range, with gaps between top-tier models narrowing to single-digit percentages. This means competition has moved from “does it work” to “how well does it work.”

Second, end-to-end long-horizon engineering tasks. These cover multiple engineering domains and focus on multi-step execution, tool invocation, and end-to-end problem solving. The value of such benchmarks lies in their alignment with real workflows: newer model generations typically achieve stable improvements of several percentage points over their predecessors, and these gains propagate to downstream real-world tasks.

From a capability spectrum perspective, the maturation path for long-horizon coding agents roughly follows: first, the ability to “fix a problem in a real codebase” (localization, understanding, minimal changes, verification), then the ability to “build a complete project from scratch” (architecture design, module decomposition, iterative development). A further requirement is output quality: generated games and web pages should not merely “run”—they should have refined visual styles, and the model should know how to select appropriate external professional tools and components to achieve the desired results. Knowing how to “leverage tools” rather than doing everything itself is an important marker of agent intelligence.

5. Same Intelligence, Higher Speed: The Engineering Value of Dual-Version Serving

Many teams are beginning to offer a dual-track serving strategy with a “standard version + fast version”: maintaining the same level of intelligence while using inference-side optimizations to increase output speed by 2–3x. The implementation relies on a common set of industry techniques: speculative decoding, KV cache management optimization, quantization, and continuous batching.

For developers, the selection guidance is straightforward:

• Interactive scenarios (pair programming, conversational agents, live demos): prioritize the fast version. Speed has a multiplicative impact on experience.
• Deep tasks (batch inference, offline research, background execution of long-horizon agents): the standard version remains irreplaceable for complex reasoning.
• In long-horizon agent tasks, output tokens can easily reach tens of thousands. A 2–3x speed improvement means reducing a task from hours to minutes.

6. Production Deployment: A Practical Checklist for Developers

First, choose the access form based on your workflow. These models are typically available through web interfaces, mobile apps, office suite integrations, coding tool integrations, and open APIs. Individual developers can start with the lowest-cost option—the web interface. Enterprise developers should prioritize evaluating API stability, rate limiting policies, and observability.

Second, pay attention to weight release plans. Releasing full model weights enables private deployment, domain fine-tuning, and security auditing, which is particularly important for data-sensitive industries. Weights are typically released some time after the official launch, so it is advisable to incorporate this into your technical selection timeline.

Third, agent workflow design. Consider the following pattern:

def run_long_horizon_agent(task, model, tools, sandbox):
    state = task.init_state()
    while not state.is_done():
        # Assemble context: goal + decision history + environment feedback
        ctx = build_context(task.goal, state.history, state.feedback)
        # Model decision: reasoning + tool invocation plan
        action = model.decide(ctx, allowed_tools=tools)
        # Execute in sandbox, collect observations and test results
        obs, passed = sandbox.execute(action)
        # Use test results as verifiable reward to update state
        state.update(action, obs, reward=passed)
        if state.steps > MAX_STEPS:   # Budget control
            state.rollback()          # Rollback on failure to avoid long-horizon drift
    return state.artifacts

Three practical points: explicitly model “acceptance tests” into the loop as verifiable rewards; set step and cost budgets for each action to prevent infinite loops; preserve rollback points so long-horizon tasks can recover to the most recent valid state upon failure.

Fourth, supporting cloud infrastructure. Production-grade agents require a reliable execution foundation: use a container platform to host sandbox environments, object storage to manage code artifacts and logs, message queues to decouple task orchestration, and logging services with monitoring and alerting to ensure end-to-end observability. For example, Baidu AI Cloud’s general-purpose cloud products—covering containers, storage, messaging, and observability—can address these requirements without developers having to build the entire infrastructure from scratch.

7. Conclusion

This round of model capability breakthroughs appears on the surface as a competition over leaderboard scores, but underneath it is a systems engineering competition. For large-scale RL to translate into real capabilities, every layer—RL framework, long-context inference, training efficiency, sandboxing, scheduling, and environment quality inspection—must be optimized in concert. For developers, understanding “where capability comes from” is more valuable than chasing every release announcement. Grasping the evolution of evaluation methodology and integrating models into your workflow through sound engineering practices is what ultimately converts model capability into productivity.