The Reward Conundrum: Why GRPO Training Collapses and What It Means for the Future of AI
Unraveling the Mystery of Mid-Training Reward Drops
Have you ever witnessed a reinforcement learning model’s reward curve soar steadily only to plummet unexpectedly midway through training? This phenomenon has become increasingly common with GRPO—a variant designed for training massive models—and it raises fundamental questions about the underlying tradeoffs in our current approaches to reinforcement learning.
The Architecture Behind Reinforcement Learning Stability
At the core of many reinforcement learning algorithms lies the Actor-Critic framework. While traditional texts often render this as “actor-critic,” some experts prefer the term “interaction of knowledge and action” to capture its essence. In this formulation, the critic (or “knowledge”) component evaluates and guides the actor (or “action”), ensuring that the policy gradients remain stable even as the system navigates a dynamic environment.
Unlike supervised learning—where data is static and well-defined—RL must contend with non-stationary environments and limited feedback from sparse reward signals. Incorporating a critic component, which leverages either state-value or action-value functions (or even more advanced techniques like the Advantage Function with a baseline), reduces variance in gradient calculations. This, in turn, mitigates the risk of the algorithm veering off course during training.
GRPO’s Design: Sacrificing Stability for Scale
GRPO’s design intentionally omits the critic network. The primary rationale is straightforward: when training models that span hundreds of billions of parameters, the memory requirements of maintaining both a large actor and a separate critic network become prohibitive. Instead, GRPO opts to estimate the advantage function online, essentially trading off computational stability for reduced storage needs.
This design tradeoff carries significant consequences. Consider the widely adopted PPO algorithm, which typically employs a value network comparable in size to the policy model. Although this setup imposes a heavy memory and computation load, it inherently stabilizes training. GRPO, on the other hand, is more prone to fluctuations—especially when batch sizes are small—resulting in those abrupt drops in reward during training. For academic research groups and teams working with medium-scale models (on the order of millions to tens of millions of parameters), the increased variance can prove to be a critical drawback.
When GRPO Falters: Lessons from Practical Training
Training observations across various model sizes bring these tradeoffs into focus. Smaller models—whether 1B or even certain 7B and 8B configurations like LLAMA—often struggle with sparse reward signals during GRPO training. In these scenarios, a single training batch may not deliver enough reward signals to maintain the stability of the policy gradient, leading to erratic performance. By contrast, larger models in the 32B to 38B range show improved stability due to denser reward feedback, and models beyond 100B tend to sidestep these issues more readily.
In practical terms, many teams have gravitated toward using PPO for smaller models because its inherent stability outweighs its computational costs. Some have even employed techniques such as model distillation to enrich reward density, thereby accelerating the training process relative to supervised fine-tuning . These insights point to a nuanced picture: GRPO may be viable when data is abundant, but it struggles when operating under tighter resource constraints.
Beyond GRPO: Shifting Paradigms in AI Training
The challenges faced by GRPO are not isolated technical quirks but reflect deeper questions about the future of reinforcement learning. Influential voices in the field—such as Richard Sutton, often referred to as the father of reinforcement learning—have long highlighted the plasticity issues inherent in deep learning methods. Recent insights confirm that traditional RL methods may eventually be supplanted by more robust alternatives.
A notable example is Anthropic’s report from March 27, 2025, titled Tracing the Thoughts of a Large Language Model. The study provides evidence for a shared abstract space—a latent space—that large models can leverage to apply learned knowledge flexibly across different contexts. This discovery reinforces the notion that training paradigms based solely on reinforcement learning might be reaching their practical limits. In many ways, RL served its transitional purpose, paving the way for approaches like semi-supervised learning that promise greater stability and long-term commercial viability.
Embracing Semi-Supervised and Unsupervised Learning
A promising path forward lies in semi-supervised learning, which blends elements of supervised and unsupervised techniques to forge robust models that learn from less explicitly labeled data. Key advantages include:
- Improved Stability and Scalability: By leveraging vast amounts of unlabeled data, semi-supervised methods can capture richer patterns and relationships without an overreliance on sparse reward signals.
- Generalization Across Contexts: Unlike RL, which is tuned for very specific interactions within a defined environment, semi-supervised learning encourages the development of models that can generalize more effectively. This is particularly important as AI systems are increasingly expected to operate across diverse domains.
Multi-Modal Learning and the Power of Shared Abstract Spaces
Anthropic's research highlights the existence of a shared abstract space—a latent domain where disparate forms of data converge into a unified representation. This breakthrough has several implications for the future of AI:
- Cross-Contextual Knowledge Application: Models capable of accessing and manipulating a shared latent space can flexibly apply knowledge learned in one context to solve problems in another. This capacity is critical for developing AI systems that are both adaptable and efficient.
- Enhanced Multi-Modal Capabilities: The integration of text, image, audio, and other sensory data into a common latent framework could lead to breakthroughs in tasks that require an understanding beyond a single modality. Such synergy is expected to underpin the next generation of AI applications in fields ranging from autonomous systems to creative content generation.
Beyond Conventional Frameworks: Integrative and Hybrid Approaches
The future of AI research is likely to be characterized by hybrid approaches that harness the best aspects of various training paradigms:
- Meta-Learning and Transfer Learning: By teaching models how to learn and adapt from one task to another, meta-learning enables a level of versatility that static RL frameworks cannot match. This approach, coupled with transfer learning, can significantly reduce the need for large amounts of task-specific data.
- Neurosymbolic Integration: Merging symbolic reasoning with deep learning promises to create systems that are not only efficient but also explainable. Neurosymbolic approaches offer pathways for integrating high-level concepts and abstract reasoning—a development that directly complements the insights gleaned from the study of latent spaces.
- Adaptive Self-Supervision: Future algorithms may increasingly rely on self-supervised learning techniques that allow models to generate their own learning signals by identifying patterns and structures within the data. Such adaptive methods are expected to bypass many of the limitations imposed by fixed reward structures in RL.
A Future in Transition
The evolution of AI training methodologies is steering away from the brittle stability of traditional reinforcement learning. As the industry faces mounting pressure to balance resource constraints with model performance, innovation will likely hinge on hybrid approaches that harmonize data richness, multi-modal inputs, and a shared abstract understanding of tasks. In this emerging paradigm, the tradeoffs seen in GRPO—where computational efficiency undermines stability—serve as a critical learning point.
In summary, GRPO’s design choices illustrate the complex balance between memory constraints and training stability. For those developing models on a smaller scale or operating under limited data conditions, algorithms with an integrated critic component, such as PPO, remain the more reliable choice. Meanwhile, as AI ventures further into multi-modal and semi-supervised territories, the industry is poised for a transformative shift in how models learn and adapt.