Post-Training Era of LLMs: RL and Long-Horizon Agent Capabilities

1. The Shift in Competitive Focus: From Pre-Training Scaling to Post-Training Alchemy

Over the past two years, the competitive logic of the LLM industry has undergone a structural change. The pre-training approach of “stacking more data and more compute” has gradually hit the ceiling of high-quality data exhaustion and diminishing marginal returns. Post-training—especially large-scale reinforcement learning (RL)—is becoming the new capability growth curve. Looking at recently released flagship models, several highly consistent technical characteristics emerge:

• Parameter scales reaching the trillions: larger base capacity provides the chassis for knowledge and reasoning;
• Context windows extended to millions of tokens: long documents, entire code repositories, and ultra-long task trajectories become first-class citizens;
• Post-training compute substantially increased: RL moves from a nice-to-have to the main battleground;
• Gains concentrated in high-value areas such as code, Agents, and multimodality: the goal is not fine-tuning chat experience but usability on real productivity tasks.

A widely circulated saying captures this well: “The only way to discover the limits of the possible is to go a little beyond them into the impossible.” It aptly describes the current post-training wave: real breakthroughs come not only from larger base models but from engineering systems that systematically convert added RL compute into stable capability gains.

2. Large-Scale RL Post-Training: It Takes the Entire Stack

Many developers reduce post-training to “designing a good reward function,” but in industrial practice that is merely the tip of the iceberg. When a team needs to run large-scale joint training across hundreds of downstream domains, any weakness in a single infrastructure layer will leave added compute spinning idle. Based on public technical disclosures, leading teams have broadly rebuilt the full stack:

1. RL training framework: decoupling sampling from training. High-throughput rollout generation and gradient update workloads have very different characteristics, requiring independent scaling and asynchronous pipelining; otherwise compute idles back and forth between “waiting for data” and “waiting for training.”
2. Long-context inference optimization. Agent task trajectories routinely span hundreds of thousands of tokens. Attention computation and KV Cache management during sampling directly determine rollout throughput, and therefore RL iteration speed.
3. Training efficiency engineering. Hybrid parallelism strategies, communication overlap, and operator fusion serve a single goal: push compute utilization up so every GPU-hour produces effective gradients.
4. Sandbox infrastructure. Code and terminal tasks must be verified in real executable environments. Massively concurrent container sandboxes, network isolation, and resource quotas are essential foundations.
5. Scheduling system. Training, sampling, evaluation, and environment construction are heterogeneous workloads sharing a cluster, requiring fine-grained priority and elastic scheduling.
6. Environment quality platform. The ceiling of RL is set by environment quality: whether task diversity is sufficient, whether verifier scoring is trustworthy, and whether there are reward loopholes that can be gamed (reward hacking) all require a platformized, shared quality-check process for continuous oversight.

The value of this system can be summed up in one sentence: ensure that every increment of RL compute translates into stable, efficient capability returns. Conversely, teams lacking infrastructure accumulation will struggle to reproduce equivalent gains even with the same amount of compute—this is the fundamental reason for model tier differentiation in the post-training era.

3. Long Context and Multimodality: From “Can Read” to “Reads Accurately”

The Real Value of Million-Token Contexts

A million-token context is not a marketing number; it maps to three high-value scenarios:

• Whole-repository code understanding: place a mid-sized repository together with dependency notes and historical discussions into context at once, letting the model refactor and troubleshoot with a global view;
• Long-cycle research analysis: scenarios such as financial research and legal due diligence require cross-referencing figures and logic across hundreds of pages of material;
• Agent memory carrier: execution trajectories and tool return results for long-horizon tasks depend on a sufficiently large context window.

Technically, this requires solving hard problems such as the quadratic complexity of attention computation, the extrapolation stability of positional encoding, and the memory and bandwidth bottlenecks of KV Cache. Equally important is evaluation methodology: single-point retrieval tests in the “needle in a haystack” style are far from sufficient. The real watershed is multi-hop reasoning—answers that require chaining together multiple pieces of information scattered across different positions in a document.

Multimodality: “Small and Sharp” Tests Beat Leaderboards

Gaps in multimodal capability often only surface on demanding real-world samples. A low-cost, reproducible test paradigm is: use the same blurry photo of a printed shopping receipt and ask different models to check whether the amounts contain errors. Clear divergence is often observed in practice—weaker models misread blurred digits (for example, reading 28.9 as 23.9), while stronger models can infer the correct reading by cross-checking line items against the subtotal. Such cases are cheap, objectively scorable, and directly target detail perception, making them ideal “health checks” before business launch.

It is worth noting that on creative tasks such as game and web visuals, the new generation of models also shows more refined stylistic control—not only knowing “what looks good” but also knowing “which professional tool to invoke to produce it.” This is a signal of aesthetic priors and tool-use capability maturing together during post-training.

4. Long-Horizon Agent Capabilities: Evaluation Paradigm Determines the Capability Ceiling

Why Static Q&A Is Insufficient for Evaluating Agents

Traditional Q&A benchmarks can only test “whether it knows,” while Agent tasks examine “whether it can get it done”: multi-step execution, tool invocation, end-to-end problem solving, and self-correction after errors. Common industry terminal-style benchmarks offer a design philosophy worth borrowing:

• Real environments: tasks are performed in real terminals and real code repositories, not simplified multiple-choice questions;
• Resource constraints: CPU, memory, and time budgets are limited, forcing the model to make engineering trade-offs under constraints;
• Dedicated verification: each task comes with strict automated scoring logic to eliminate “guessing the right answer.”

Building Your Own Evaluation Set with Executable Scoring

For developers, rather than worshiping public leaderboards, it is better to build a regression-ready evaluation set for core business scenarios. Below is a minimal viable illustrative framework:

# Illustrative code: a minimal regression evaluation framework for model capability
import json
from dataclasses import dataclass, field
@dataclass
class Case:
    case_id: str          # case identifier
    modality: str         # text / vision / terminal
    prompt: str           # instruction sent to the model
    asset: str = ""       # associated image, code repository, or sandbox environment
    verifier: str = ""    # automated scoring script
    expect: dict = field(default_factory=dict)
def run_case(case: Case, endpoint: str) -> dict:
    resp = invoke_model(endpoint, case)     # call the model under test (illustrative)
    ok, detail = run_verifier(case, resp)   # executable scoring to avoid subjective human drift
    return {"case_id": case.case_id, "passed": ok, "detail": detail}
if __name__ == "__main__":
    cases = load_cases("eval_sets/receipt_and_terminal.jsonl")
    print(json.dumps([run_case(c, CFG_ENDPOINT) for c in cases],
                     ensure_ascii=False, indent=2))

For terminal-type tasks, cases can be executed in resource-constrained sandboxes with automatic verification:

# Illustrative: run a terminal task in a constrained sandbox and score automatically
run_sandbox \
  --cpus=2 --memory=2g --timeout=600 \
  --task ./tasks/log_fix \
  --entry "bash solve_task.sh && python verify.py"

Public evaluation results show that leading open-weight models have reached or approached the level of the strongest closed-source models on such coding and Agent benchmarks, with gaps on some benchmarks narrowing to within one or two percentage points. Public results also show that on long-horizon benchmarks covering multiple engineering domains and testing multi-step execution and end-to-end problem solving, the new generation of models achieved a significant leap of roughly five to six hours of percentage points over the previous generation. The rigor of the evaluation paradigm is becoming the yardstick for a team’s technical caliber.

5. Inference Efficiency: Same Intelligence, Higher Speed

Beyond intelligence level, output speed directly determines product experience and unit cost. A common recent practice is to offer a high-speed version alongside the flagship model: intelligence roughly aligned, with output speed increased by about 2–3x. Widely used speedup techniques include:

• Quantization: compress weights and activations under controlled precision loss to reduce memory footprint and improve bandwidth utilization;
• Speculative decoding: a small model drafts and a large model verifies, emitting multiple tokens per forward pass;
• KV Cache paging and management: reduce memory fragmentation and improve throughput in concurrent and long-context scenarios;
• Distillation and downsizing: distill small, high-speed variants from the flagship model;
• Continuous batching and scheduling optimization: reduce queuing latency and improve overall cluster throughput.

A simple principle for selection: for interactive scenarios, prioritize first-token latency and token generation speed; for offline batch processing, prioritize throughput and unit price. Pairing high-speed and standard versions often achieves a better balance between experience and cost.

6. Open Weights: A Key Variable for Enterprise Adoption

A notable release cadence in the industry is: launch the online service first, then open the full model weights at an agreed time. Open weights mean three things for enterprise users:

1. Private deployment: sensitive data stays within the domain, meeting compliance requirements;
2. Secondary development: continue training or fine-tune on business data to accumulate proprietary capabilities;
3. Controllable cost: for stable, high-volume workloads, the marginal cost of a self-built inference cluster may beat pay-as-you-go API calls.

Of course, evaluate rationally: open weights do not equal out-of-the-box readiness. Hardware procurement, inference framework adaptation, and high-availability operations are all real engineering investments that must be weighed against the team’s current situation.

7. A Developer’s Evaluation and Selection Checklist

Based on the analysis above, here is an actionable selection checklist:

1. Build a golden evaluation set from real business data, covering both routine samples and “small and sharp” hard samples (such as blurry receipts);
2. Insist on automated scoring, replacing human grading with executable verification to keep results regression-ready and comparable;
3. Focus on long-horizon tasks: multi-step execution, tool invocation, and failure recovery, rather than single-turn Q&A alone;
4. Weigh quality, speed, and cost across three dimensions, stress-testing interactive and batch scenarios separately;
5. Watch open-weight plans and deployment paths, preserving optionality for private deployment and fine-tuning;
6. Leverage cloud infrastructure: model service platforms such as Baidu AI Cloud typically provide general capabilities including model hosting, fine-tuning, inference deployment, and monitoring and alerting. Combined with object storage for evaluation data, container services for running sandboxes, and log services for tracing Agent execution trajectories, teams can focus on business logic rather than infrastructure.

8. Conclusion

Competition in the post-training era is fundamentally a three-in-one contest of “model + infrastructure + evaluation system.” Trillion-parameter scale and million-token contexts define the capability ceiling; large-scale RL determines how much of that ceiling can be filled; and rigorous evaluation determines whether you can actually put those capabilities to use. For developers, rather than chasing every number on a leaderboard, it is better to return to your own business scenarios: build a trustworthy evaluation set so that every model upgrade is grounded in evidence—that is the small step beyond the impossible.