Evolution of Ultra-Large-Scale Models: Long-Horizon Agents and Efficient Inference

1. Evolution Direction and Technical Challenges of Ultra-Large-Scale Models

As AI technology advances into deeper waters, leading ultra-large-scale parameter models (such as those reaching trillion-level parameter scales) are no longer focused solely on improving static benchmark scores. Instead, they are shifting strategic priorities toward “long-horizon agent” capabilities and the deployment of complex multi-domain tasks. Currently, mainstream models generally support context windows of millions of tokens, giving them the foundational capability to handle code generation, 3D creation, in-depth financial research, and agent tasks that require sustained execution over extended periods.

However, truly enabling models to work continuously in real business environments presents enormous technical challenges. The core tension lies in the博弈 between the upper bound of model intelligence and the bottlenecks of post-training compute and infrastructure. To break through this ceiling, the industry is scaling up post-training compute and extending reinforcement learning (RL) to hundreds of downstream vertical domains to improve model robustness and accuracy on specific tasks. This strategy demands not only strong generalization capability from the models themselves but also stringent requirements on underlying training frameworks and inference infrastructure.

2. Systematic Overhaul of RL Frameworks and Infrastructure Upgrades

In the process of extending reinforcement learning to hundreds of downstream domains, traditional training frameworks can no longer meet the demands of large-scale joint training. To enable models to perform at near-human-expert levels in scenarios such as code writing, tool invocation, and financial analysis, technical teams must carry out systematic, deep modifications to the underlying architecture.

1. Optimization of RL Frameworks and Long-Context Inference

Running reinforcement learning in long-context environments (e.g., millions of tokens) causes memory usage and computational latency to rise exponentially. Optimization directions typically include adopting efficient attention mechanism variants (such as sparse attention or ring attention mechanisms) and introducing asynchronous computation paradigms into RL frameworks, allowing policy network and value network updates to be decoupled. Additionally, for long-horizon tasks, models need multi-turn reflection and self-correction capabilities, which requires RL frameworks to design reward functions that focus not only on final outcomes but also on process-level rewards for intermediate reasoning steps.

2. Sandbox Infrastructure and Environment Quality Checks

Long-horizon agent execution depends on operating within real computing environments, such as terminal command execution, code compilation, and runtime. Building high-isolation, high-throughput sandbox infrastructure therefore becomes critical. Sandbox environments need to support rapid instantiation and destruction to ensure that agents conducting tens of thousands of trial-and-error training iterations do not damage the host system. Meanwhile, environment quality-check platforms must automatically evaluate the correctness of execution results within sandboxes. For example, in code generation tasks, the sandbox must not only compile code but also feed real Pass/Fail signals back to the RL model through predefined unit test cases.

3. Scheduling Systems and Training Efficiency Improvement

Running RL training simultaneously across hundreds of downstream domains involves massive heterogeneous compute resource allocation. Scheduling systems need fine-grained resource awareness, orchestrating CPU-intensive environment construction tasks with GPU-intensive model inference/training tasks in a pipeline to maximize cluster GPU utilization. On container platforms and high-performance computing clusters—such as those offered by Baidu AI Cloud—elastic scaling and GPU memory sharing technologies can effectively support such large-scale, high-concurrency joint training tasks.

3. Capability Leaps and Practical Pain Points of Long-Horizon Agents

The core of long-horizon agents lies in “sustained operation” and “autonomous planning.” Taking coding agents and financial research agents as examples, such tasks typically require the model to autonomously decompose tasks, invoke external tools (such as terminals, APIs, databases), analyze returned results, and iterate strategies after receiving instructions—a process that may last tens of minutes or even hours.

1. The Two-Way Convergence of Code and Agent Capabilities

In industry-recognized terminal coding benchmarks, top models have achieved scores approaching the 60–70% range when completing complex tasks in resource-constrained real terminal environments. This achievement relies heavily on the closed-loop capabilities of “tool invocation–result analysis–code correction” accumulated during the post-training phase. However, in actual enterprise applications, code generation still faces pain points of “imbalance” and “verbosity.” Some models perform excellently in specific languages (such as Python) but poorly in system-level languages (such as C++/Rust); meanwhile, models tend to generate overly detailed comments and defensive code, resulting in verbose output tokens that directly impact API call costs and response speed.

2. Enterprise-Grade Hosted Agents Services

To address the pain point of developers struggling to maintain 7×24 agent operation locally, the industry has begun offering Hosted Agents services. These services provide enterprises with the Harness (runtime environment) that supports long-running agents as cloud infrastructure. They are responsible not only for maintaining persistent agent process operation but also for supporting multi-agent collaboration. For example, in a complex financial research scenario, a master agent can distribute subtasks to a data-scraping agent, a statistical analysis agent, and a report generation agent, with the cloud-hosted environment handling message routing and state synchronization among them. Such services typically rely on cloud-native message queues and object storage to achieve state persistence.

4. High-Speed Inference Engines and API Call Cost Control

No matter how powerful a model is, if output speed is too slow or quota consumption is too rapid, it cannot achieve a commercial closed loop on the enterprise side. Currently, API call costs and response latency of ultra-large-scale models are core pain points for developers.

1. Cache Hit Mechanisms and Cost Optimization

To reduce developer call costs, the industry has widely introduced context caching mechanisms. When input tokens in a request hit the cache, the billing unit price is significantly reduced (typically one-tenth of the non-cache-hit price or even lower). For long-horizon agents, since each round of interaction needs to carry the complete history of previous interactions, improving cache hit rates can significantly reduce developers’ financial burden. Developers can maximize the use of cloud caching mechanisms by rationally designing prompt structures—placing fixed system instructions and background knowledge at the front of the request body.

2. Engineering Implementation of High-Speed Versions

Improving output speed by 2–3× while maintaining the same level of model intelligence is a major engineering victory. This typically relies on speculative decoding, continuous batching, and low-level operator optimization. Although the absolute unit price of high-speed APIs may be higher, in end-to-end tasks, because they significantly reduce time-to-first-token and overall generation time, agents can enter the next round of decision-making faster—potentially reducing overall compute costs at the task level. Enterprises can weigh the trade-off between standard and high-speed versions based on their business sensitivity to latency.

5. Summary and Best Practice Recommendations

The evolution of ultra-large-scale parameter models toward long-horizon agent capabilities marks AI’s leap from “conversational tool” to “productivity agent.” For enterprises and developers integrating such models, the following points are recommended:

1. Prioritize post-training capability alignment: When selecting models or building agents, evaluate not only base parameter counts but also post-training RL performance in specific vertical domains (such as code and finance).
2. Build robust sandboxes and toolchains: Leverage cloud container services and monitoring/alerting systems to build safe, observable execution environments for agents, preventing uncontrolled side effects during long-horizon operations.
3. Implement fine-grained cost management: Fully utilize API providers’ cache billing strategies and optimize prompt structures; flexibly switch between standard and high-speed model versions for business steps with different latency requirements to achieve the optimal balance of cost and efficiency.