Reinforcement Learning and AI’s “Second Half”

Over the last decade, AI progress has been dominated by a simple recipe: invent a better architecture, scrape a larger dataset, and pre-train at scale. Convolutional nets, LSTMs, and eventually Transformers rode that wave, pushing benchmark scores higher with each generation of models.

But by 2025, frontier systems like GPT-4-class models and their peers have mostly saturated standard benchmarks. Scaling still helps, but every extra parameter and token delivers less obvious gain. That has led many researchers to argue that we are entering the “second half” of AI—a phase where pre-training is the starting line, not the finish line.

In this second half, Reinforcement Learning (RL) is increasingly seen as the central mechanism for turning powerful but passive models into active agents: systems that can set goals, take actions, learn from feedback, and improve through experience. Pre-training builds the prior; reinforcement learning decides what to do with it.

This article explains:

• What the “second half” of AI actually means
• Why reinforcement learning is uniquely suited to this new phase
• Top real-world milestones showing RL working beyond static datasets
• How RL is reshaping evaluation, infrastructure and research priorities
• What challenges remain as we scale RL to frontier models

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What Is the “Second Half” of AI and Why Does It Favor RL?

What Changed After a Decade of Pre-Training?

In the “first half” of modern AI, improvements came from:

• New architectures (convnets → LSTMs → Transformers)
• Larger and more diverse datasets
• Self-supervised objectives that turned the open internet into training fuel

Benchmarks were mostly static: image datasets, language understanding suites, leaderboards for translation, summarization, and more. The objective was straightforward: minimize loss or maximize accuracy on fixed test sets.

Now, several things have shifted:

• Frontier LLMs already match or exceed human-level performance on many classic NLP benchmarks.
• Marginal gains from simply “add more data and parameters” are smaller and more expensive.
• Many of the most important tasks—agentic workflows, tools, autonomy—cannot be captured by one-shot evaluation.

The result is a growing consensus:

If the first half of AI was about representation learning on static corpora, the second half is about decision-making in interactive environments.

That is precisely the domain of reinforcement learning.

Why Reinforcement Learning Fits the Second Half

Supervised and self-supervised learning answer the question:

“Given this input, what should the next token/label be?”

Reinforcement learning asks a different question:

“Given this state, what action should an agent take to maximize long-term reward?”

This subtle difference has major consequences:

• Agency: RL is built around actions and consequences, not just predictions.
• Long-horizon reasoning: Rewards can depend on sequences of decisions, not single outputs.
• Adaptation: Agents can keep learning from new experience, not just from a frozen dataset.

In the second half of AI, where we care about tool use, planning, robustness, safety, and real-world utility, these properties are not optional—they are central. RL is the natural framework for them.

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Why Reinforcement Learning Unlocks Capabilities Beyond Supervised LLMs

What Supervised Pre-Training Gives Us—and What It Doesn’t

Modern LLMs trained on trillions of tokens excel at:

• Language understanding and generation
• Pattern recognition across domains (code, natural language, structured text)
• Few-shot generalization to tasks they were never explicitly trained on

However, a purely pre-trained model still has limitations:

• It reacts to prompts, but does not independently set goals.
• It does not inherently know when to call tools, or how to coordinate multi-step workflows.
• It has no built-in mechanism to optimize for long-term outcomes like user satisfaction, safety, or task completion.

Supervised fine-tuning helps align behavior, but it is still tied to static labels or human-authored examples.

How RL Turns LLMs into Agents

Reinforcement learning, especially when built on top of strong LLM priors, provides the missing ingredients:

Goal-directed behavior

• Define a reward signal (pass a test, fix a bug, satisfy a rubric, get positive human feedback).
• Train the model to select actions that maximize this reward over time.

Multi-step reasoning and self-correction

• Allow the model to break a task into sub-tasks, call external tools, inspect partial results, and revise its approach.
• Reward trajectories where the agent checks its own work and converges to correct answers.

Alignment with human preferences

• In RL from human feedback (RLHF), humans or learned reward models score responses.
• The agent learns to internalize these preferences: being helpful, truthful, harmless, and on-topic.

We have already seen this pattern:

• ChatGPT-style systems: Major quality leaps from GPT-3 to widely deployed assistants came largely from RLHF, not entirely new architectures.
• Agentic models like Kimi K2: RL on tool-using, long-horizon tasks trains models to be deliberate, cautious, and self-verifying, rather than merely fluent.

RL, in other words, is how we turn a pre-trained “pattern recognizer” into a coherent, goal-seeking agent.

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Top Real-World Breakthroughs Showing RL’s Impact Beyond Benchmarks

To understand why RL is taking center stage, it helps to look beyond synthetic leaderboards. Below are five illustrative domains where RL has already demonstrated transformative impact.

1. How RL Mastered Games and Self-Play Environments

Deep RL first drew global attention with game-playing systems:

• AlphaGo / AlphaZero: Learned to play Go and chess at superhuman level purely from self-play, discovering strategies even world champions had never seen.
• OpenAI Five: Trained via massive self-play RL to dominate professional teams in the complex multi-agent game Dota 2.

Key lessons:

• Given a well-shaped reward (win/lose, score difference), RL agents can iterate through millions of simulated games and discover non-obvious strategies.
• Self-play avoids exhaustive labeling and instead uses competition as a generator of experience.

These systems foreshadow what happens when we place LLM-based agents in sufficiently rich simulated environments with clear feedback signals.

2. How RL Controls Complex Physical Systems Like Fusion Reactors

Reinforcement learning has also moved into experimental physics:

• Deep RL agents have been deployed to control fusion plasmas in tokamak reactors, learning to manipulate magnetic fields in real time to confine and shape plasma.

This is a textbook long-horizon control problem:

• The system is high-dimensional, unstable and non-linear.
• Human-crafted controllers struggle to adapt to the full space of possible configurations.

RL, trained first in simulation and then transferred to the real reactor, learned policies that could safely and robustly manage the plasma, opening a path toward AI-assisted scientific instruments.

3. What RL Achieves in Negotiation and Multi-Agent Social Settings

Meta’s CICERO system, which reached human-level performance in the strategy game Diplomacy, combines:

• A large language model for natural negotiation and communication
• A planning module trained via RL to make strategic decisions, model other players, and coordinate actions

Diplomacy requires trust-building, alliance formation, deception, and adaptation—all in a multi-agent setting. CICERO’s success signals that RL can:

• Handle strategic interaction in social environments
• Integrate language, planning, and game theory into a cohesive agent

Such capabilities are directly relevant to future AI systems that must navigate negotiations, markets, or complex multi-stakeholder settings.

4. How RL Is Powering the Next Wave of Space Robotics

Recent years have seen RL leave the lab and operate in orbit:

• On the International Space Station, RL controllers have flown free-flying robots (such as Astrobee) in microgravity, performing autonomous maneuvers after training in simulation.
• A small university-built satellite has successfully executed onboard attitude control with a deep RL policy, proving that a controller trained on Earth can govern a spacecraft’s orientation in real space conditions.

These milestones are remarkable for several reasons:

• Space is unforgiving—mistakes are expensive or irrecoverable.
• Traditional controllers are hand-crafted and tuned over months; RL offers an alternative that can learn complex policies faster and adapt more flexibly.
• Successful sim-to-real transfer in space strengthens confidence that RL will be applicable to terrestrial robotics, autonomous vehicles, and industrial control systems.

5. How RL Is Becoming the Default for Aligning and Customizing Foundation Models

On the LLM side, RL is now a standard toolchain component:

• RLHF is widely used to polish raw base models into helpful assistants.
• New startups and labs are building infrastructure for automated RL fine-tuning of frontier models, betting that the next wave of value will come from letting organizations sculpt model behavior with task-specific reward signals.

From this perspective, RL is not an exotic research trick; it is becoming the primary mechanism by which foundation models are adapted to concrete products and domains.

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How RL Is Changing Evaluation, Benchmarks, and Agent Design

Why Static Benchmarks Are No Longer Enough

Traditional benchmarks assume:

• A fixed dataset
• i.i.d. samples from a known distribution
• A one-shot mapping from input to output

But agentic systems break these assumptions:

• The agent chooses which actions to take, changing its own future observations.
• The environment may be non-stationary (users, markets, adversaries react).
• Success depends on process (how you get there), not just the final answer.

In the second half of AI, we increasingly care about:

• Task completion in long workflows
• Human satisfaction over sustained interaction
• Safety under distribution shift
• Cumulative reward in open-ended settings

These criteria cannot be captured by a handful of static test sets. They require interactive evaluations, often with humans in the loop, rich simulators, or live deployment metrics.

How RL Forces Better Environments and Metrics

By design, RL training requires:

• Environments where agents can act and experience consequences
• Reward functions or feedback channels that reflect what we value

This pushes the field toward:

• Building more realistic simulators for code, tools, robotics, markets, and social interactions.
• Designing better reward models based on human preference data, safety constraints, and domain expertise.
• Treating evaluation as an ongoing process, not a one-time leaderboard submission.

In that sense, the rise of RL is not only a change in algorithms; it is a change in how we think about progress itself.

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Best Practices and Challenges for Scaling RL with Frontier Models

Why Scaling RL Is Harder Than Scaling Pre-Training

Despite its promise, RL is not plug-and-play:

• Training is often unstable: small changes in reward, exploration, or environment can derail learning.
• Sample complexity can be huge: agents might need millions or billions of timesteps to reach strong performance.
• Real-world environments are expensive to interact with; we cannot simulate everything at the scale of internet text.

These challenges become more acute when:

• Policies are represented by trillion-parameter models
• Environments are high-stakes (finance, healthcare, critical infrastructure)

How the Community Is Addressing These Challenges

To make RL tractable at scale, researchers and engineers are investing in:

• Better RL optimizers and distributed training schemes that stabilize learning for very large models and reduce hardware requirements.
• Sim2real transfer pipelines that allow most learning to happen in simulation, with careful adaptation before deployment.
• Hybrid methods that combine RL with supervised learning, imitation learning, and language modeling—using demonstrations, offline logs, and reward models to jump-start training.
• Safer exploration techniques that constrain behavior during learning, especially in high-risk domains.

There is also growing interest in mixed paradigms, such as:

• Using LLMs as planners and RL policies as controllers.
• Having language models write or critique reward functions and evaluation rubrics.
• Combining RL with diffusion-based text generation to explore and refine candidate solutions in latent space before committing to a single trajectory.

These approaches suggest that the “second half” will not be RL instead of pre-training, but RL on top of and intertwined with foundation models.

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Conclusion: Why Reinforcement Learning Is Poised to Drive AI’s Second Half

Reinforcement learning is rising to prominence at a very particular moment:

• We now possess immensely capable pre-trained models, rich in knowledge and patterns.
• We also have environments and tools where those models can act: browsers, code runners, robots, satellites, and more.
• What we lack—and what RL provides—is a systematic way to turn this potential into goal-directed, adaptive, and aligned behavior.

In the first half of AI, representation learning and static benchmarks carried us an astonishing distance. But as we push toward AI that can:

• Reason across multiple steps
• Use tools and APIs intelligently
• Operate safely in unstructured environments
• Learn from experience and human feedback over time

it is becoming clear that what got us here will not get us there.

Reinforcement learning is not magic, and it is not easy. It demands better environments, better feedback, and better infrastructure. Yet precisely because it forces us to confront these hard problems—agency, long-horizon credit assignment, safety, and evaluation—it is likely to be the driving force of AI’s second half.

Pre-training built the brain.

Reinforcement learning teaches it how to act.