Demystifying Policy Optimization in RL: An Introduction to PPO and GRPO

Introduction

learning (RL) has achieved remarkable success in teaching agents to resolve complex tasks, from mastering Atari games and Go to training helpful language models. Two necessary techniques behind a lot of these advances are policy optimization algorithms called Proximal Policy Optimization (PPO) and the newer Generalized Reinforcement Policy Optimization (GRPO). In this text, we’ll explain what these algorithms are, why they matter, and the way they work – in beginner-friendly terms. We’ll start with a fast overview of reinforcement learning and Policy Gradient methods, then introduce GRPO (including its motivation and core ideas), and dive deeper into PPO’s design, math, and benefits. Along the way in which, we’ll compare PPO (and GRPO) with other popular RL algorithms like DQN, A3C, TRPO, and DDPG. Finally, we’ll take a look at some code to see how PPO is utilized in practice. Let’s start!

Background: Reinforcement Learning and Policy Gradients

Reinforcement learning is a framework where an agent learns by interacting with an environment through trial and error. The agent observes the state of the environment, takes an motion, after which receives a reward signal and possibly a brand new state in return. Over time, by trying actions and observing rewards, the agent adapts its behaviour to maximise the cumulative reward it receives​. This loop of is the essence of RL, and the agent’s goal is to find policy (a technique of selecting actions based on states) that yields high rewards.

In policy-based RL methods (also often called policy gradient methods), we directly optimize the agent’s policy. As a substitute of learning “value” estimates for every state or state-action (as in value-based methods like Q-learning), policy gradient algorithms adjust the parameters of a policy (often a neural network) within the direction that improves performance. A classic example is the REINFORCE algorithm, which updates the policy parameters in proportion to the reward-weighted gradient of the log-policy​. In practice, to scale back variance, we use an advantage function (the additional reward of taking motion in state in comparison with average) or a baseline (like a price function) when computing the gradient​. This results in actor-critic methods, where the “actor” is the policy being learned, and the “critic” is a price function that estimates how good states (or state-action pairs) are to offer a baseline for the actor’s updates​. Many advanced algorithms, including Ppo, fall into this actor-critic family: they maintain a policy (actor) and use a learned value function (critic) to help the policy update.

Generalized Reinforcement Policy Optimization (GRPO)

One in every of the newer developments in policy optimization is Generalized Reinforcement Policy Optimization (GRPO) – sometimes referred to in literature as Group Relative Policy Optimization. GRPO was introduced in recent research (notably by the DeepSeek team) to handle some limitations of PPO when training large models (equivalent to language models for reasoning)​. At its core, GRPO is a variant of policy gradient RL that eliminates the necessity for a separate critic/value network and as an alternative optimizes the policy by comparing a of motion outcomes against one another.

Motivation: Why remove the critic? In complex environments (e.g. long text generation tasks), training a price function might be hard and resource-intensive. By “foregoing the critic,” GRPO avoids the challenges of learning an accurate value model and saves roughly half the memory/computation since we don’t maintain extra model parameters for the critic​. This makes RL training simpler and more feasible in memory-constrained settings. In actual fact, GRPO was shown to chop the compute requirements for Reinforcement Learning from human feedback nearly in half in comparison with PPO​.

Core idea: As a substitute of counting on a critic to inform us how good each motion was, GRPO evaluates the policy by comparing multiple actions’ outcomes relative to one another. Imagine the agent (policy) generates a set of possible outcomes for a similar state (or prompt) a of responses. These are all evaluated by the environment or a reward function, yielding rewards. GRPO then computes a bonus for every motion based on how its reward compares to the others. One easy way is to take each motion’s reward minus the typical reward of the group (optionally dividing by the group’s reward standard deviation for normalization)​. This tells us which actions did higher than average and which did worse. The policy is then updated to assign higher probability to the better-than-average actions and lower probability to the more severe ones​. In essence, ​.

How does this look in practice? It seems the loss/objective in GRPO looks very much like PPO’s. GRPO still uses the concept of a “surrogate” objective with probability ratios (we’ll explain this under PPO) and even uses the identical clipping mechanism to limit how far the policy moves in a single update​. The important thing difference is that the advantage is computed from these group-based relative rewards quite than a separate value estimator​. Also, implementations of GRPO often include a KL-divergence term within the loss to maintain the brand new policy near a reference (or old) policy, much like PPO’s optional KL penalty​.

PPO vs. GRPO — Top: In PPO, the agent’s is trained with the assistance of a separate (critic) to estimate advantage, together with a and a hard and fast (for KL penalty). Bottom: GRPO removes the worth network and as an alternative computes benefits by comparing a bunch of sampled outcomes reward scores for a similar input via an easy “group computation.” The policy update then uses these relative scores because the advantage signals. By dropping the worth model, GRPO significantly simplifies the training pipeline and reduces memory usage, at the associated fee of using more samples per update (to form the groups)

In summary, GRPO might be seen as a PPO-like approach and not using a learned critic. It trades off some sample efficiency (because it needs multiple samples from the identical state to check rewards) in exchange for greater simplicity and stability when value function learning is difficult. Originally designed for big language model training with human feedback (where getting reliable value estimates is hard), GRPO’s ideas are more generally applicable to other RL scenarios where relative comparisons across a batch of actions might be made. By understanding GRPO at a high level, we also set the stage for understanding PPO, since GRPO is actually built on PPO’s foundation.

Proximal Policy Optimization (PPO)

Now let’s turn to Proximal Policy Optimization (PPO) – one of the popular and successful policy gradient algorithms in modern RL. PPO was introduced by OpenAI in 2017 as a solution to a practical query: In other words, we would like big improvement steps without “falling off a cliff” in performance. Its predecessors, like Trust Region Policy Optimization (TRPO), tackled this by enforcing a tough constraint on the scale of the policy update (using complex second-order optimization). PPO achieves an identical effect in a much simpler way – using first-order gradient updates with a clever clipped objective – which is less complicated to implement and empirically just pretty much as good.

In practice, PPO is implemented as an on-policy actor-critic algorithm. A typical PPO training iteration looks like this:

1. Run the present policy within the environment to gather a batch of trajectories (state, motion, reward sequences). For instance, play 2048 steps of the sport or have the agent simulate just a few episodes.
2. Use the collected data to compute the advantage for every state-action (often using Generalized Advantage Estimation (GAE) or an identical method to mix the critic’s value predictions with actual rewards).
3. Update the policy by maximizing the PPO objective above (often by gradient ascent, which in practice means doing just a few epochs of stochastic gradient descent on the collected batch).
4. Optionally, update the worth function (critic) by minimizing a price loss, since PPO typically trains the critic concurrently to enhance advantage estimates.

Because PPO is on-policy (it uses fresh data from the present policy for every update), it forgoes the sample efficiency of off-policy algorithms like DQN. Nevertheless, PPO often makes up for this by being stable and scalable it’s easy to parallelize (collect data from multiple environment instances) and doesn’t require complex experience replay or goal networks. It has been shown to work robustly across many domains (robotics, games, etc.) with relatively minimal hyperparameter tuning. In actual fact, PPO became something of a default alternative for a lot of RL problems as a consequence of its reliability.

PPO variants: There are two primary variants of PPO that were discussed in the unique papers:

• which adds a penalty to the target proportional to the KL-divergence between recent and old policy (and adapts this penalty coefficient during training). That is closer in spirit to TRPO’s approach (keep KL small by explicit penalty).
• which is the variant we described above using clipped objective and no explicit KL term. That is by far the more popular version and what people often mean by “PPO”.

Each variants aim to limit policy change; PPO-clip became standard due to its simplicity and robust performance. PPO also typically includes entropy bonus regularization (to encourage exploration by not making the policy too deterministic too quickly) and other practical tweaks, but those are details beyond our scope here.

Why PPO is popular – benefits: To sum up, PPO offers a compelling mixture of stability and simplicity. It doesn’t collapse or diverge easily during training due to clipped updates, and yet it’s much easier to implement than older trust-region methods. Researchers and practitioners have used PPO for every little thing from controlling robots to training game-playing agents. Notably, PPO (with slight modifications) was utilized in OpenAI’s InstructGPT and other large-scale RL from human feedback projects to fine-tune language models, as a consequence of its stability in handling high-dimensional motion spaces like text. It might not at all times be absolutely the most sample-efficient or fastest-learning algorithm on every task, but when unsure, PPO is usually a reliable alternative.

PPO and GRPO vs Other RL Algorithms

To place things in perspective, let’s briefly compare PPO (and by extension GRPO) with another popular RL algorithms, highlighting key differences:

• DQN (Deep Q-Network, 2015): DQN is a method, not a policy gradient. It learns a Q-value function (via deep neural network) for discrete actions, and the policy is implicitly “take the motion with highest Q”. DQN uses tricks like an experience (to reuse past experiences and break correlations) and a (to stabilize Q-value updates). Unlike PPO which is on-policy and updates a parametric policy directly, DQN is off-policy and doesn’t parameterize a policy in any respect (the policy is greedy w.r.t. Q). PPO typically handles large or continuous motion spaces higher than DQN, whereas DQN excels in discrete problems (like Atari) and might be more sample-efficient because of replay.
• A3C (Asynchronous Advantage Actor-Critic, 2016): A3C is an earlier policy gradient/actor-critic algorithm that uses multiple employee agents in parallel to gather experience and update a world model asynchronously. Each employee runs by itself environment instance, and their updates are aggregated to a central set of parameters. This parallelism decorrelates data and quickens learning, helping to stabilize training in comparison with a single agent running sequentially. A3C uses a bonus actor-critic update (often with n-step returns) but doesn’t have the specific “clipping” mechanism of PPO. In actual fact, PPO might be seen as an evolution of ideas from A3C/A2C – it retains the on-policy advantage actor-critic approach but adds the surrogate clipping to enhance stability. Empirically, PPO tends to outperform A3C, because it did on many Atari games with far less wall-clock training time, as a consequence of more efficient use of batch updates (A2C, a synchronous version of A3C, plus PPO’s clipping equals strong performance). A3C’s asynchronous approach is less common now, since you’ll be able to achieve similar advantages with batched environments and stable algorithms like PPO.
• TRPO (Trust Region Policy Optimization, 2015): TRPO is the direct predecessor of PPO. It introduced the concept of a “trust region” constraint on policy updates essentially ensuring the brand new policy will not be too removed from the old policy by enforcing a constraint on the KL divergence between them. TRPO uses a fancy optimization (solving a constrained optimization problem with a KL constraint) and requires computing approximate second order gradients (via conjugate gradient). It was a breakthrough in enabling larger policy updates without chaos, and it improved stability and reliability over vanilla policy gradient. Nevertheless, TRPO is complicated to implement and might be slower as a consequence of the second-order math. PPO was born as an easier, more efficient alternative that achieves similar results with first-order methods. As a substitute of a tough KL constraint, PPO either softens it right into a penalty or replaces it with the clip method. Consequently, PPO is less complicated to make use of and has largely supplanted TRPO in practice. By way of performance, PPO and TRPO often achieve comparable returns, but PPO’s simplicity gives it an edge for development speed. (Within the context of GRPO: GRPO’s update rule is actually a PPO-like update, so it also advantages from these insights with no need TRPO’s machinery).
• DDPG (Deep Deterministic Policy Gradient, 2015): DDPG is an algorithm for continuous motion spaces. It combines ideas from DQN and policy gradients. DDPG maintains two networks: a critic (like DQN’s Q-function) and an actor that deterministically outputs an motion. During training, DDPG uses a replay buffer and a goal network (like DQN) for stability, and it updates the actor using the gradient of the Q-function (hence “deterministic policy gradient”). In easy terms, DDPG extends Q-learning to continuous actions through the use of a differentiable policy (actor) to pick out actions, and it learns that policy by gradients through the Q critic. The downside is that off-policy actor-critic methods like DDPG might be somewhat finicky – they might get stuck in local optima or diverge without careful tuning (improvements like TD3 and SAC were later developed to handle a few of DDPG’s weaknesses). In comparison with PPO, DDPG might be more sample-efficient (replaying experiences) and may converge to deterministic policies which may be optimal in noise-free settings, but PPO’s on-policy nature and stochastic policy could make it more robust in environments requiring exploration. In practice, for continuous control tasks, one might select PPO for ease and robustness or DDPG/TD3/SAC for efficiency and performance if tuned well.

In summary, PPO (and GRPO) vs others: PPO is an on-policy, policy gradient method focused on stable updates, whereas DQN and DDPG are off-policy value-based or actor-critic methods focused on sample efficiency. A3C/A2C are earlier on-policy actor-critic methods that introduced useful tricks like multi-environment training, but PPO improved on their stability. TRPO laid the theoretical groundwork for protected policy updates, and PPO made it practical. GRPO, being a derivative of PPO, shares PPO’s benefits but simplifies the pipeline further by removing the worth function making it an intriguing option for scenarios like large-scale language model training where using a price network is problematic. Each algorithm has its own area of interest, but PPO’s general reliability is why it’s often a baseline alternative in lots of comparisons.

PPO in Practice: Code Example

To solidify our understanding, let’s see a fast example of how one would use PPO in practice. We’ll use a preferred RL library (Stable Baselines3) and train an easy agent on a classic control task (CartPole). This instance might be in Python using PyTorch under the hood, but you won’t must implement the PPO update equations yourself – the library handles it.

Within the code above, we first create the CartPole environment (a classic balancing pole toy problem). We then create a PPO model with an MLP (multi-layer perceptron) policy network. Under the hood, this sets up each the policy (actor) and value function (critic) networks. Calling model.learn(...) launches the training loop: the agent will interact with the environment, collect observations, calculate benefits, and update its policy using the PPO algorithm. The verbose=1 just prints out training progress. After training, we run a fast test: the agent uses its learned policy (model.predict(obs)) to pick out actions and we step through the environment to see the way it performs. If all went well, the CartPole should balance for an honest variety of steps.

import gymnasium as gym
from stable_baselines3 import PPO

env = gym.make("CartPole-v1")

model = PPO(policy="MlpPolicy", env=env, verbose=1)

model.learn(total_timesteps=50000)

# Test the trained agent
obs, _ = env.reset()
for step in range(1000):
    motion, _state = model.predict(obs, deterministic=True)
    obs, reward, terminated, truncated, info = env.step(motion)
    if terminated or truncated:
        obs, _ = env.reset()

This instance is intentionally easy and domain-generic. In additional complex environments, you may need to regulate hyperparameters (just like the clipping, learning rate, or use reward normalization) for PPO to work well. However the high-level usage stays the identical define your environment, pick the PPO algorithm, and train. PPO’s relative simplicity means you don’t must fiddle with replay buffers or other machinery, making it a convenient place to begin for a lot of problems.

Conclusion

In this text, we explored the landscape of policy optimization in reinforcement learning through the lens of PPO and GRPO. We began with a refresher on how RL works and why policy gradient methods are useful for directly optimizing decision policies. We then introduced GRPO, learning the way it forgoes a critic and as an alternative learns from relative comparisons in a bunch of actions – a technique that brings efficiency and ease in certain settings. We took a deep dive into PPO, understanding its clipped surrogate objective and why that helps maintain training stability. We also compared these algorithms to other well-known approaches (DQN, A3C, TRPO, DDPG), to spotlight when and why one might select policy gradient methods like PPO/GRPO over others.

Each PPO and GRPO exemplify a core theme in modern RL: find ways to get big learning improvements while avoiding instability. PPO does this with gentle nudges (clipped updates), and GRPO does it by simplifying what we learn (no value network, just relative rewards). As you proceed your RL journey, keep these principles in mind. Whether you’re training a game agent or a conversational AI, methods like PPO have turn into go-to workhorses, and newer variants like GRPO show that there’s still room to innovate on stability and efficiency.

Sources:

1. Sutton, R. & Barto, A. . (Background on RL basics).
2. Schulman et al. . arXiv:1707.06347 (PPO original paper).
3. OpenAI Spinning Up – (PPO explanation and equations).
4. RLHF Handbook – (Details on GRPO formulation and intuition).
5. Stable Baselines3 Documentation(DQN description) (PPO vs others).