Reinforcement learning vs “regular” training: the real difference is not the math, it is the loop

Most ML people grow up on a simple mental model: you have a dataset, you define a loss, you run gradient descent, you ship a checkpoint. That covers supervised learning and a lot of self-supervised pretraining. The model is learning from a fixed distribution of examples, and the training pipeline is basically a linear flow from data to gradients.

Reinforcement learning (RL) breaks that mental model because the model is not only learning from data, it is also actively creating the data it learns from via interaction. RL is learning in a feedback system: the policy produces actions, the environment reacts, a reward signal is produced, and that interaction generates the training signal. This loop is the reason RL feels “qualitatively” different from regular training, even though you still do gradient updates.

A useful way to state it is: regular training optimizes predictive accuracy on a dataset; RL optimizes decision quality over trajectories. The unit of competence changes from “can you predict the next thing” to “can you take a sequence of actions that leads to a better outcome over time.” The standard reference framing this distinction is Sutton and Barto’s textbook, which formalizes RL as learning to maximize expected cumulative reward through interaction.

RL is still “regular training”, but the data pipeline is endogenous

You said something important and very underappreciated in casual discussions: in RL you are still doing regular training. There is still a parametric model, there are still gradients, there is still optimization. The difference is the provenance of the examples and how tightly coupled they are to the current model.

In supervised training, the dataset is exogenous. It exists independent of your current checkpoint. In RL, the trajectory distribution depends on your current policy. Change the policy, you change the data you will see next. This coupling creates the core systems and stability challenges: you are constantly chasing a moving target because the act of learning changes the data generator.

This is why many RL algorithms are described as alternating between sampling and optimizing. PPO, a widely used policy-gradient method, is literally presented as alternating between collecting data via environment interaction and then optimizing a surrogate objective over that collected data.

Why RL became the “post-training” lever for LLM behavior

RL’s recent hype is not random. In the LLM era, base pretraining gives broad capability, but it does not necessarily produce the behavior you want when the model is deployed as an assistant or an agent. RL became attractive because it gives a clean interface for shaping behavior around a goal signal.

RLHF is the canonical example: you collect human preference comparisons, learn a reward model, then optimize the policy to produce outputs humans prefer. InstructGPT made this pipeline mainstream and showed a smaller model could be preferred over a much larger one after the RLHF stage.

Then the conversation shifted from “alignment and helpfulness” to “reasoning and agency.” OpenAI explicitly described o1 as being trained with reinforcement learning for complex reasoning. DeepSeek-R1 made an even sharper claim in the open literature: reasoning behaviors can be incentivized through reinforcement learning and emerge as patterns like self-reflection and verification.

So RL’s popularity is downstream of an empirical observation: once you have a capable model, RL-like objective shaping can change how it behaves under pressure, especially on long-horizon tasks that look more like sequential decision making than pure next-token prediction.

The coding-agent example is the clearest “systems truth” about RL

Take your coding agent loop. You want the model to do things like clone a GitHub repo, change directories, apply patches, compile, run tests, read logs, then decide the next action. That is not a single inference call. That is a multi-step closed loop with a stateful environment.

At each step:

The model produces an action (a tool call, a command, a patch, a plan step).

A sandboxed environment executes that action (docker container, filesystem, compiler, test runner).

The environment returns a new state (stdout, stderr, exit codes, diffs, test failures).

The model conditions on the new state and chooses the next action.

Now you can define rewards in many ways: tests passing, fewer failing tests, compilation success, speed, or even shaped intermediate rewards. But the key point is that “data generation” is inseparable from environment execution. The environment is part of the training pipeline.

This is where RL becomes fundamentally more complex than regular training from a systems perspective. You are not only scaling a trainer. You are scaling an interactive factory that produces trajectories, and that factory includes tools, isolation, scheduling, and reliability.

RL infrastructure is training + inference + environments, all scaled together

In practice, RL is rarely bottlenecked by a single thing. It is bottlenecked by whichever component is currently slowest across three coupled subsystems:

Training: backprop, optimizer, parameter updates, gradient aggregation.

Inference: rollout generation, sampling multiple candidates, longer contexts, tool planning overhead.

Environments: tool execution, simulation, compilers, browsers, databases, external dependencies, and the latency of state transitions.

You cannot treat these as a single homogeneous workload. They often want different hardware and different scaling strategies. Training wants large GPU clusters and high utilization. Environment execution may want CPUs, fast storage, lots of containers, and strict sandboxing. Inference wants GPUs too, but its throughput and latency characteristics are different from training. If any part stalls, the loop degrades.

This is why RL feels like distributed systems engineering, not just ML engineering. The “algorithm” is only half the story. The rest is building a reliable, high-throughput interaction machine.

Why stability is harder in RL than in supervised learning

Supervised learning is mostly a stationary optimization problem: you can shuffle the same dataset forever and measure loss curves. RL is a non-stationary feedback system. If you push too hard, the policy can collapse. If you accidentally optimize a proxy, the agent can exploit it.

Classic RL success stories highlight the power and the fragility of this setup. Deep Q-Networks (DQN) achieved human-level control on Atari by learning policies directly from pixels, but it required careful engineering of replay buffers, target networks, and stabilizers. AlphaGo combined supervised learning from human games with reinforcement learning from self-play, showing that RL can push beyond imitation, but it also demonstrates the amount of scaffolding needed around the core idea.

With LLM agents, you inherit all the classic RL issues plus new ones: long horizons, sparse rewards, tool reliability, stochastic environments, and the ability of the model to “game” your evaluation harness.

Reward design becomes product design, security engineering, and science all at once

In supervised learning, your labels are usually the ground truth target. In RL, your reward is the specification. That sounds clean until you realize most real goals are hard to specify precisely, especially for open-ended tasks like coding, research, or multi-tool workflows.

Reward misspecification creates reward hacking: the agent finds a way to get reward without doing what you meant. In a coding environment, maybe it edits the tests instead of fixing the bug. In a browser environment, maybe it exploits a cached response. In a “helpfulness” objective, maybe it learns to sound confident rather than be correct.

This is why RL systems often become security-adjacent. You need sandboxing, immutable evaluation, logging, and red-team style thinking. The RL loop is an adversary if you give it the wrong incentives.

RL is also about explainability now, because trust depends on understanding the policy

Your note about “EXPLAIN” maps cleanly to a growing research area: explainable reinforcement learning (often abbreviated XRL). As RL moves into real products, labs, and regulated domains, you need to explain why an agent chose an action, what it believed would happen, and what reward signal shaped that behavior.

Surveys on XRL emphasize that explanations can target different parts of the loop: the model, the reward, the state, or the task structure. A key theme across these surveys is that many explanation methods approximate or simplify a complex policy after the fact, rather than making the policy inherently interpretable from the start.

For LLM agents, explainability is not just a UX feature. It is an auditing primitive. If you are training an agent to operate tools, you need postmortems: which observation caused the action, which tool output changed the plan, and whether the agent is exploiting quirks in the environment. That becomes essential for debugging and for safety.

The simplest summary that stays true at scale

If you want a single sentence that captures your entire prompt without losing the systems reality:

Supervised learning trains on a dataset; reinforcement learning trains on an interaction loop, where the model must repeatedly act, observe the environment’s response, and use the outcomes as the training signal, which forces you to scale training, inference, and environments as one coupled system.

That is why RL is “regular training plus more.” Not more math. More system. More feedback. More failure modes. More levers for shaping behavior. And that is exactly why it has become the backbone for modern reasoning and agentic post-training, even as it remains one of the hardest paradigms to implement robustly.

References

Bekkemoen, Y., Hauge, J.B. and Mehlum, K. (2024) ‘Explainable reinforcement learning (XRL): a systematic literature review’, *Machine Learning*.

Guo, D. et al. (2025) ‘DeepSeek-R1: Incentivizing Reasoning Capability in LLMs through Reinforcement Learning’, *Nature*.

Guo, D. et al. (2025) *DeepSeek-R1: Incentivizing Reasoning Capability in LLMs through Reinforcement Learning* (arXiv preprint).

Mnih, V. et al. (2015) ‘Human-level control through deep reinforcement learning’, *Nature*, 518, pp. 529–533.

OpenAI (2024) ‘Learning to reason with LLMs’.

Ouyang, L. et al. (2022) ‘Training language models to follow instructions with human feedback’, *NeurIPS 2022*.

Puiutta, E. and Veith, E.M. (2020) ‘Explainable Reinforcement Learning: A Survey’, *arXiv* (2005.06247).

Qing, Y. et al. (2022) ‘A Survey on Explainable Reinforcement Learning’, *arXiv* (2211.06665).

Rafailov, R. et al. (2023) ‘Direct Preference Optimization: Your Language Model is Secretly a Reward Model’, *NeurIPS 2023*.

Schulman, J. et al. (2017) ‘Proximal Policy Optimization Algorithms’, *arXiv* (1707.06347).

Silver, D. et al. (2016) ‘Mastering the game of Go with deep neural networks and tree search’, *Nature*, 529, pp. 484–489.

Sutton, R.S. and Barto, A.G. (2018) *Reinforcement Learning: An Introduction*. 2nd edn. Cambridge, MA: MIT Press.

* Sutton and Barto (2018) RL book (MIT Press page): [https://mitpress.mit.edu/9780262039246/reinforcement-learning/](https://mitpress.mit.edu/9780262039246/reinforcement-learning/)

* Sutton and Barto (2018) RL book (free draft site): [https://incompleteideas.net/book/the-book-2nd.html](https://incompleteideas.net/book/the-book-2nd.html)

* Schulman et al. (2017) PPO (arXiv): [https://arxiv.org/abs/1707.06347](https://arxiv.org/abs/1707.06347)

* Ouyang et al. (2022) InstructGPT / RLHF (arXiv): [https://arxiv.org/abs/2203.02155](https://arxiv.org/abs/2203.02155)

* OpenAI (2024) Learning to reason with LLMs: [https://openai.com/index/learning-to-reason-with-llms/](https://openai.com/index/learning-to-reason-with-llms/)

* DeepSeek-R1 (arXiv PDF): [https://arxiv.org/pdf/2501.12948](https://arxiv.org/pdf/2501.12948)

* DeepSeek-R1 (arXiv abstract): [https://arxiv.org/abs/2501.12948](https://arxiv.org/abs/2501.12948)

* DeepSeek-R1 (Nature): [https://www.nature.com/articles/s41586-025-09422-z](https://www.nature.com/articles/s41586-025-09422-z)

* Mnih et al. (2015) DQN Nature: [https://www.nature.com/articles/nature14236](https://www.nature.com/articles/nature14236)

* Silver et al. (2016) AlphaGo Nature: [https://www.nature.com/articles/nature16961](https://www.nature.com/articles/nature16961)

* Rafailov et al. (2023) DPO (arXiv): [https://arxiv.org/abs/2305.18290](https://arxiv.org/abs/2305.18290)

* Puiutta and Veith (2020) XRL survey (arXiv): [https://arxiv.org/abs/2005.06247](https://arxiv.org/abs/2005.06247)

* Qing et al. (2022) XRL survey (arXiv PDF): [https://arxiv.org/pdf/2211.06665](https://arxiv.org/pdf/2211.06665)

* Bekkemoen et al. (Springer link for XRL SLR): [https://link.springer.com/article/10.1007/s10994-023-06479-7](https://link.springer.com/article/10.1007/s10994-023-06479-7)