Upgrading Long-Horizon Agent Capabilities in Very Large Reasoning Models: Engineering Practices

1. Background and Direction of the New Generation of Large Model Upgrades

Large model technology has undergone a clear paradigm shift over the past year. Pure text generation and conversational ability are no longer the primary competitive focus. Instead, what matters is the ability of models to complete complex tasks in real business environments. This capability is commonly referred to as agent capability. It requires models not only to understand instructions, but also to possess a combination of skills including task decomposition, tool invocation, environment interaction, error recovery, and long-term state maintenance.

From a technical evolution perspective, the core driver of this shift comes from changes in post-training investment. Traditional supervised fine-tuning mainly addresses the question of whether a model can produce correct answers, while reinforcement learning is now being used to address whether a model can consistently make correct decisions in uncertain environments. The key difference is that the latter requires the model to evaluate long-term returns rather than focusing only on the quality of single-step outputs.

When model parameters reach the trillion scale and context windows extend to millions of tokens, several dimensions become critical for measuring a new generation of large models: how to efficiently inject domain-specific feedback signals during training, how to reliably support long-running tasks during inference, and how to provide an operable agent hosting environment on the engineering side. This article discusses these three aspects.

2. Post-Training Strategy: From General Alignment to Domain-Specific Reinforcement Learning

2.1 Expanding Reinforcement Learning Across Domains

The most notable change in post-training for the new generation of large models is the expansion of reinforcement learning coverage from a small number of general tasks to hundreds of downstream domains. The rationale is straightforward: task environments vary greatly across domains, and decision preferences learned in one type of environment often do not transfer directly to another.

Code generation is a good example. A model that performs well on competitive programming problems does not necessarily perform well in a real terminal environment involving multi-file modifications, dependency installation, test execution, and debugging. The latter requires the model to understand dynamic feedback such as file system state, command execution results, and error output, and adjust subsequent actions accordingly.

Therefore, post-training requires a joint training framework that covers multiple domains. The key components of this framework include:

• Task sampler: Continuously samples interactive tasks from different domains to ensure diversity and appropriate difficulty distribution in training data.
• Environment simulator: Provides executable environment feedback for each task, such as sandboxed terminals, API simulators, and database query interfaces.
• Reward model: Computes rewards based on multi-dimensional signals such as task completion rate, execution efficiency, and error recovery count.
• Policy optimizer: Continuously updates the model’s decision policy while maintaining base capabilities without degradation.

The engineering challenge of such large-scale joint training lies in the significant variation in task execution time across domains. A simple knowledge Q&A task may take only a few seconds, while a complete financial research report generation task may take hours. The training scheduling system needs to dynamically balance resource usage across different task types to avoid long-tail tasks blocking the training pipeline.

2.2 Coordinated Infrastructure Changes

Supporting the above training strategy requires systematic adjustments to the underlying infrastructure. From an engineering practice perspective, the following four types of infrastructure changes are particularly critical:

Sandboxed execution environments: In reinforcement learning training, every action produced by the model must be executed in a controlled environment to obtain feedback. Sandbox environments need to support rapid creation, state snapshots, resource isolation, and high-concurrency batch execution. For code-related tasks, common language runtimes and package management tools should be pre-installed. For data analysis tasks, data source access permissions and computing resources need to be provisioned in advance.

Scheduling systems: Training tasks have evolved from a single forward pass into a multi-stage pipeline of model inference, environment execution, reward computation, and gradient updates. Resource requirements vary significantly across stages, so scheduling systems need to support unified management of heterogeneous resource pools and dynamically allocate compute based on task priority.

Environment quality inspection platforms: Reinforcement learning is highly sensitive to the quality of training environments. Noise, latency anomalies, or incorrect reward signals in the environment can all negatively affect model policy. An independent environment quality inspection platform is therefore needed to continuously monitor environment health and automatically isolate problematic nodes when anomalies occur.

Long-context training optimization: Training with million-token context windows requires special memory management strategies. Traditional attention computation faces memory bottlenecks in long-sequence scenarios, requiring techniques such as sequence parallelism, sparse attention, and gradient checkpointing to reduce per-GPU memory usage.

3. Capability Evaluation: Evolution and Limitations of Benchmark Systems

3.1 From Static Evaluation to Dynamic Environment Evaluation

Traditional large model evaluation relies primarily on static datasets: given an input, the model produces an output, which is then compared against a reference answer. This approach is suitable for evaluating knowledge coverage and short-range reasoning ability, but it poorly reflects a model’s comprehensive performance in real environments.

For agent capability evaluation, the industry is gradually shifting toward dynamic environment evaluation. Terminal environment benchmarks are a representative example. These tests place models in resource-constrained real terminals and require them to complete tasks consisting of multiple operational steps. Each task includes dedicated tests to verify whether the final result is correct. The core value of this evaluation approach lies in:

• Authenticity: Tasks are derived from abstractions of real development scenarios rather than artificially constructed simplified problems.
• Verifiability: The result of each task can be objectively verified through automated test scripts.
• Process completeness: The model must fully traverse the cycle of understanding the task, formulating a plan, executing operations, and verifying results.

Public data shows that new-generation large models have approached the performance of leading closed-source models on such evaluations, with the score gap narrowing to within one percentage point. This indicates that targeted injection of domain-specific reinforcement learning signals during post-training can indeed substantially improve a model’s real-world capabilities.

3.2 Evaluation Challenges for Long-Horizon Code Tasks

Long-horizon code tasks are those that cannot be completed by generating a code snippet in a single pass. They typically involve understanding, modifying, debugging, and verifying existing codebases. Evaluating such tasks presents several unique challenges:

Task granularity: When a task contains dozens of sub-steps, defining what constitutes partial completion becomes difficult. Overly fine-grained scoring criteria may lead models to pursue local optima while neglecting the overall objective.

Environment consistency: Differences in pre-installed dependency versions, network access permissions, and file system states across evaluation environments can cause score fluctuations for the same model. Evaluation platforms need substantial engineering effort to ensure environment consistency.

Time cost: Evaluating long-horizon tasks takes far longer than single-turn Q&A, which limits evaluation scale. Careful task sampling strategies are needed to obtain statistically meaningful conclusions within limited evaluation scale.

4. Inference Deployment and API Cost Structure

4.1 Context Caching and Tiered Pricing

An important optimization direction for long-context models during inference is context caching. When a model needs to process large instruction templates, codebase content, or conversation history, repeatedly computing attention states for these prefix contents on every request causes significant waste.

The basic logic of cache-hit pricing is that tokens identical to already-computed cached content are billed at a lower rate, while only new tokens and output tokens are billed at standard rates. This pricing model has a substantial impact on actual developer costs. If each request from an agent application needs to carry 500,000 tokens of system prompts and tool definitions, and most of this content remains unchanged across requests, cache hits can significantly reduce input costs.

Looking at general pricing structures, cache-hit input prices are typically about one-tenth of non-cached input prices, while output prices are significantly higher than input prices. This price structure reflects the computational resource allocation characteristics of the inference stage: output tokens must be generated sequentially token by token and cannot be processed in parallel like input tokens, resulting in higher unit costs.

4.2 Design Considerations for High-Speed Inference Versions

Providing high-speed inference versions while maintaining identical model capabilities centers on reducing latency by optimizing inference engine scheduling strategies and hardware utilization. Common techniques include:

• Speculative decoding: Using a small draft model to quickly generate candidate token sequences, which are then verified in parallel by the large model, thereby reducing the number of sequential generation steps.
• Batching optimization: Merging inference computations from multiple concurrent requests into larger matrix operations on the server side to improve GPU utilization.
• KV cache quantization: Applying low-precision quantization to key-value caches to reduce memory bandwidth usage and accelerate decoding.

High-speed versions are typically targeted at latency-sensitive scenarios such as interactive programming assistance and real-time data queries. Their price is typically around twice that of standard versions, reflecting the additional resource cost incurred on the server side to reduce latency.

When choosing between standard and high-speed versions, developers need to weigh the latency tolerance and cost constraints of their specific scenarios. For batch processing tasks or background long-running agents, the standard version is usually the more economical choice. For interactive scenarios requiring real-time feedback, the experience improvement from high-speed versions is often worth the additional cost.

5. Managed Agent Services: Bringing Long-Horizon Tasks from Lab to Production

5.1 The Runtime Foundation for Long-Horizon Agents

When applying large models to real business processes, a key challenge is managing sustained task execution. Under the traditional API call model, the client is responsible for managing conversation state, triggering subsequent calls, and handling timeouts and retries. This approach is acceptable for short-term tasks, but for long-horizon tasks that take hours or even days to complete, client-side management complexity rises sharply.

The managed agent service approach is to provide the infrastructure required to support long-running agents—including task queues, state persistence, tool invocation gateways, and exception recovery mechanisms—as a cloud service to developers. Developers only need to define task objectives and available tools without worrying about infrastructure details during task execution.

This service model supports multi-agent collaboration, where multiple agents with different responsibilities share the same task context and coordinate through message passing. For example, in a financial research scenario, one agent can be configured for data collection, another for data analysis, and a third for report writing, with shared intermediate artifacts and coordination through task state.

5.2 Technical Guarantees for 7x24 Task Execution

Supporting uninterrupted 7x24 task execution requires technical guarantees at multiple levels:

State persistence: Agent execution state needs to be periodically snapshotted and durably stored. When infrastructure failures occur, the system needs to recover from the most recent snapshot rather than restarting the task from scratch. This requires introducing an event sourcing pattern at the state management level, recording all state changes as an immutable event stream.

Reliable tool invocation encapsulation: External tools invoked by agents may fail due to network jitter, service rate limiting, or input anomalies. The managed service needs to provide unified retry, degradation, and circuit-breaking strategies for tool invocations to prevent a single tool failure from causing the entire task to fail.

Dynamic resource allocation: Long-horizon tasks are not always in resource-intensive states. The system needs to dynamically allocate computing resources based on the current phase of the task, avoiding resource waste while the task is waiting for external events.

Observability design: For tasks running over multiple days, developers need real-time visibility into task progress, current bottlenecks, and historical decision processes. The managed service needs to provide complete execution logs, status monitoring, and critical-node alerting capabilities.

6. Practical Recommendations for Developers

For developers planning to integrate new-generation large model capabilities into their business systems, the following practical recommendations are worth considering:

Evaluate first and avoid over-reliance on a single metric: Before selecting a model, evaluate it on a task set similar to your business scenario. Focus on end-to-end completion rates in real environments rather than only scores on public leaderboards.

Optimize context structure to reduce inference costs: By streamlining system prompts, organizing tool definitions efficiently, and reusing static content across requests, you can significantly improve cache hit rates and reduce per-task costs.

Design recoverable execution flows for long-horizon tasks: No matter how capable the model is, long-running tasks may still be interrupted by environmental fluctuations. Design idempotent tool invocation interfaces and resumable task flows on the business side to complement the state management capabilities of managed services.

Establish task classification and resource tiering strategies: Tier tasks by complexity, time sensitivity, and cost sensitivity, and select appropriate inference versions and hosting approaches for each tier to achieve a balance between performance and cost.

As post-training methodologies continue to evolve, there remains significant room for improvement in large models’ ability to work continuously in complex environments. For developers, this means updating engineering practices accordingly—shifting from how to call a model to how to design systems that work collaboratively with models. The depth and breadth of this shift will determine the true ceiling of value that large model technology can create in real business applications.