Reinforcement Learning Benchmarks and Architecture Considerations: The Critical Role of Activation Functions and Emerging Industry Developments

As reinforcement learning (RL) systems continue to evolve, tackling increasingly complex environments—ranging from embodied robotics to multi-agent coordination and long-horizon reasoning—the importance of rigorous benchmarking and meticulous architectural design has never been more evident. Recent developments highlight that seemingly minor design choices, such as the selection of activation functions within value networks, can significantly influence an agent’s stability, robustness, and overall performance. Simultaneously, industry advances and technological innovations are shaping the future landscape of RL research, emphasizing the necessity to adapt evaluation protocols and architectural best practices accordingly.

The Crucial Impact of Activation Functions in RL Value Networks

A growing body of empirical evidence underscores that activation functions like SiLU (Sigmoid Linear Unit) and GELU (Gaussian Error Linear Unit)—despite their popularity in transformer architectures and supervised learning—may adversely affect RL value network performance, especially in scenarios demanding long-term reasoning, embodied interaction, or multi-agent coordination.

Industry Insights and Anecdotal Evidence

Veteran game programmer and AI researcher John Carmack recently shared observations from his experimentation:

"I always lost performance when I tried to use SiLU or GELU activations in my RL value networks."

This anecdote suggests that, despite their advantages in smoother gradients and modern architectures, these nonlinearities might introduce gradient instability or nonlinearities that hinder long-horizon value estimation in RL contexts. The insight is especially relevant in tasks where stability over extended sequences is critical.

Broader Empirical Trends

Beyond Carmack’s experience, recent benchmarks in embodied AI and world-model research reinforce that activation function choice is a non-trivial architectural decision. For example:

• Memory-intensive architectures, such as Full-Motion Transformers and 4D reasoning models, require activation functions that support longer, stable temporal dependencies.
• Multi-agent frameworks like Grok 4.2 demonstrate that robust value representations are essential for enabling meaningful reasoning among multiple agents, where performance can degrade if activation functions induce instability or gradient issues.

Implications for Benchmark Design and Evaluation Protocols

Given these findings, it is crucial that benchmarking efforts treat activation functions as a primary variable in the evaluation process. Recommendations include:

• Explicitly vary activation functions (e.g., ReLU vs SiLU/GELU) across experiments to assess their impact on long-horizon and embodied tasks.
• Include challenging scenarios involving long sequences, multi-modal inputs, or embodied interactions, as these are most sensitive to architectural stability.
• Standardize experimental controls—keep hyperparameters, network topology, and training procedures consistent, altering only the activation functions—to isolate their effects.

Such practices will enable the community to distill the true influence of architectural nuances and guide the development of more resilient RL systems.

Industry and Infrastructure Support for Advanced RL Architectures

The push toward more sophisticated RL systems is bolstered by significant technological advances and industry initiatives:

• Hardware acceleration plays a pivotal role. For example, Taalas HC1 chips now enable processing of thousands of tokens per second, facilitating real-time long-horizon evaluation and deployment of embodied agents. This makes activation stability even more critical, as the hardware demands reliable, consistent value estimation over extended sequences.
• Multi-modal and memory-based architectures—such as full-motion transformers and 4D reasoning models—are increasingly prominent. They underscore the necessity for activation functions that support longer temporal dependencies without degrading performance.
• Agent demos and robotics: Recent demonstrations, including quadruped and humanoid robots showcased at events like the AI Impact Summit 2026, highlight practical stakes. Reliable activation choices are essential for deploying robust, real-world agents capable of sustained reasoning and interaction.

Notable Industry Developments

Recent funding and research initiatives further exemplify the momentum:

• AI chip startup MatX raised $500 million in Series B funding to develop specialized LLM training chips, emphasizing the importance of hardware tailored for large-scale, long-horizon models.
• Advances in multimodal models—such as Qwen3.5 Flash, recently launched on platforms like Poe—highlight ongoing efforts to optimize processing for both text and images, demanding architectures that maintain stability across modalities.
• Cutting-edge research from organizations like Meta continues to interpret complex physical phenomena in video, advancing 4D reasoning capabilities that rely heavily on activation stability for temporal coherence.

Current Status and Future Directions

The convergence of empirical findings, industry innovations, and infrastructural advancements signals a paradigm shift in RL research:

• Architectural subtleties, once considered minor, are now recognized as critical determinants of robustness and scalability—particularly in long-horizon, embodied, multi-agent tasks.
• Benchmark protocols are evolving to explicitly document activation choices and systematically evaluate their impacts, fostering more transparent and reliable comparisons.
• The community is actively seeking systematic studies and alternative activation schemes that balance the benefits of modern nonlinearities with the stability demands of RL value networks.

Practical Guidance

Until comprehensive studies establish definitive alternatives, conservative practices—such as defaulting to ReLU or similar simple nonlinearities—remain advisable for value networks. Researchers are encouraged to:

• Prioritize stability in activation functions for critical applications.
• Design benchmarks that include diverse, long-horizon, embodied, and multi-modal scenarios.
• Document architectural choices transparently to facilitate reproducibility and comparative analysis.

Final Thoughts

As RL systems are increasingly deployed in embodied robotics, multi-agent environments, and long-duration reasoning tasks, activation function stability emerges as a pivotal factor in ensuring agent robustness and performance. Industry advances—spanning hardware innovations, multimodal models, and real-world demos—underscore the urgency of integrating these considerations into benchmarking and architectural design.

Moving forward, the community must embrace systematic experimentation, transparent documentation, and cross-disciplinary collaboration to develop RL systems capable of reliable, scalable, and real-world deployment. The careful selection of activation functions, coupled with rigorous evaluation, will be central to unlocking the next generation of intelligent agents.