Infrastructure
By 2026, RLVR (Reinforcement Learning with Verifiable Rewards) had become an important and rapidly adopted post-training approach for reasoning models, particularly in domains such as mathematics and coding where outcomes can be checked programmatically. Large RLVR workloads may execute hundreds or thousands of rollouts concurrently, depending on model size, batch configuration, task duration, and training architecture. Code-execution and tool-use RLVR workloads commonly use isolated environments, while simpler tasks may use deterministic or model-based graders without provisioning a separate sandbox for every sample. Choosing the right secure sandbox infrastructure can dramatically accelerate your RLVR workflows while maintaining the security and observability needed for production systems. This guide compares seven sandbox and RL-environment platforms that can support some portion of an RLVR workflow, starting with Modal, a serverless AI infrastructure platform that provides fast Sandbox scheduling and can rapidly launch GPU containers for supported configurations.
Modal delivers a serverless AI infrastructure platform where Sandboxes and training infrastructure are offered within the same stack. Modal offers inference, training, batch processing, and Sandboxes within one platform. Lovable and Quora use Modal Sandboxes at production scale, while customers such as Applied Compute and Cognition use Modal for reinforcement-learning infrastructure, and Ramp runs background coding agents on Modal Sandboxes that write code changes back into commits or pull requests. Modal has documented production reinforcement-learning workloads and first-party examples using formal verification to generate verifiable rewards.
Modal's sandbox architecture is purpose-built for AI-generated code execution and agent-based systems. The platform provides dynamically defined containers with gVisor-based isolation. Modal containerizes and virtualizes compute jobs using gVisor, providing a hardened isolation boundary for untrusted code. Sandboxes are secure by default and cannot access other Modal resources or accept inbound connections unless explicitly configured.
Modal has completed a SOC 2 Type II audit with no deviations found, and supports HIPAA-compliant workloads on Enterprise plans via a BAA. The platform uses TLS 1.3 for public APIs and encrypts data in transit and at rest.
Modal's architecture supports the full RLVR lifecycle, from environment creation through reward verification to model training. Modal supports serverless multi-node GPU training and provides high-bandwidth accelerator networking for B200, H200, and H100 clusters, with documentation describing a 3,200 Gbps RoCE RDMA network.
Best For: Teams seeking production-grade RLVR infrastructure where sandboxes, model training, and inference serving exist within the same serverless platform, substantially reducing the infrastructure overhead of coordinating separate systems.
Daytona provides secure sandbox infrastructure with an emphasis on stateful "computers" for agent-based systems. In a 2026 interview, Daytona's CEO reported that RL and evaluation workloads grew from 0% to roughly half of usage within months (a company-reported figure).
Daytona supports cold starts for its sandboxes. In a May 2026 interview, Daytona's CEO reported that its largest customer runs nearly 850,000 sandboxes per day. These figures are company-reported.
Daytona publishes an official example for TRL (Transformers Reinforcement Learning) with GRPO (Group Relative Policy Optimization) training, providing a documented path to implement reward-based fine-tuning workflows. The example connects Hugging Face TRL's GRPOTrainer to 500 Daytona sandboxes.
Best For: Teams prioritizing a documented TRL-based RLVR workflow and those working with TRL GRPO training pipelines.
HUD offers an environment-first platform for defining RL tasks, rewards, and running evaluations at scale. The platform wraps real software and code as agent-callable tools in isolated containers.
HUD's approach centers on environment definition: define an environment once, run any agent against it for evaluation, and train models on the resulting trajectories. This design philosophy aligns directly with RLVR's need for consistent, verifiable reward signals.
HUD is an environment, evaluation, and training platform with support for computer-use, browser, shell, and tool-using agents, and it is applicable to RLVR scenarios involving GUI interactions, web automation, and tool-use verification. Its protocol supports shells, browsers, tools, custom harnesses, and subagents.
Best For: Teams building computer-use and tool-using agents requiring a structured environment SDK for defining tasks, rewards, and evaluation criteria with built-in benchmark support.
Prime Intellect provides an open-source RL stack with an Environments Hub. Prime Intellect reported more than 1,000 contributed environments in April 2026, and its launch article states that more than 30 researchers and companies contributed during private beta.
Prime Intellect represents the open alternative to closed research tooling from major AI labs. The ecosystem includes the Verifiers library, the prime-rl training framework, hosted RL post-training, evaluations, inference, and on-demand GPU access.
Prime Intellect trained INTELLECT-3, a 106B-parameter mixture-of-experts model, using SFT and large-scale RL with environments built on Verifiers and hosted on the Environments Hub, demonstrating the platform's capability for frontier model development. Its reported training infrastructure used 512 H200 GPUs across 64 nodes.
Best For: Research teams and organizations requiring transparent, open-source RL infrastructure with access to a large community-driven environment ecosystem.
Runloop delivers cloud-hosted micro-VM devboxes with a dedicated custom scorer framework for RLVR reward functions. The platform positions itself as execution infrastructure for agent builders and model labs.
Runloop's distinguishing feature is its scorer customization system: changing the scoring function directly changes the reward signal for the agent. The platform passes scenario context to custom Bash scorers through the RL_SCORER_CONTEXT environment variable.
Runloop provides agent execution, repeatable benchmark scenarios, and reward computation. Teams generally connect the resulting scores and trajectories to their own RL, RFT, or other training infrastructure, since Runloop's documentation notes that policy optimization typically happens in the customer's own training infrastructure.
Best For: Teams requiring fine-grained control over reward function implementation and those building benchmark-driven RL workflows with custom evaluation logic that feeds external policy-optimization systems.
SandboxFusion is an open-source code sandbox developed by ByteDance's Seed team, supporting approximately 20 language and runtime configurations including Python, C++, Go, Java, TypeScript, Rust, and CUDA, plus a separate Python GPU mode for GPU-accelerated workloads.
SandboxFusion provides broad language coverage, with built-in online judge support for HumanEval, MBPP, CodeContests, and other standard coding benchmarks.
SandboxFusion has been used with veRL-based training projects and is configured by the DemyAgent/Open-AgentRL workflow. veRL's former in-tree reference implementation has been removed, and current users follow the documented integration pattern. The Gen-Verse project configures SandboxFusion for code execution in its DemyAgent/Open-AgentRL implementation.
Best For: Teams requiring multi-language code execution with GPU support and those integrating with open-source RL training frameworks like veRL.
OpenSandbox provides general-purpose sandbox infrastructure for AI applications. OpenSandbox originated at Alibaba and is now developed in the opensandbox-group GitHub organization, while several published SDK packages retain Alibaba namespaces. Its repository states that it is listed in the CNCF Cloud Native Landscape, which is a catalog listing rather than CNCF certification, endorsement, or CNCF project status.
OpenSandbox offers broad SDK coverage with support for Python, Java/Kotlin, JavaScript/TypeScript, C#/.NET, and Go, enabling RLVR integration across diverse tech stacks.
OpenSandbox is an open-source project with Alibaba origins and supports deployments on Docker and Kubernetes. Production reliability depends on the operator's architecture and deployment practices. Kubernetes support enables deployment across many cloud and on-premises Kubernetes environments, subject to the project's runtime and infrastructure requirements.
Best For: Enterprise teams requiring multi-language SDK support and Kubernetes-based deployment across many cloud or on-premises environments.
RLVR training can demand massive parallelism. Modal has demonstrated scaling from zero to 1,000 GPUs in minutes, and in some cases seconds, while Daytona's CEO has reported nearly 850,000 daily sandbox executions at its largest customer (a company-reported figure). Consider your expected concurrent session count and whether the platform can grow with your training needs.
For enterprise RLVR deployments, security controls matter. Modal's SOC 2 Type II audit and gVisor isolation add a hardened isolation boundary when running untrusted AI-generated code. Evaluate whether your compliance requirements call for specific reports or certifications.
GPU availability varies by provider and deployment model. Modal provides access to H100, A100, L40S, H200, and B200 GPUs through its GPU infrastructure. SandboxFusion supports CUDA, and Daytona documents GPU sandboxes using H100, H200, RTX Pro 6000, RTX 4090, and RTX 5090. GPU availability is relevant when rollout execution, inference, simulation, or reward computation requires acceleration, but it is not by itself a measure of production readiness.
Consider how each sandbox integrates with your existing RL stack. Daytona provides an official TRL GRPO example, Prime Intellect's first-party integrations center on Verifiers, the Environments Hub, prime-rl, and built-in Harbor task support, and Runloop provides custom scorer frameworks. Modal's approach of combining sandboxes with training infrastructure in one platform can substantially reduce integration overhead.
Modal describes itself as the only platform where sandboxes and training infrastructure are native to the same stack. Combining Sandboxes, training, inference, storage, and batch compute on Modal can substantially reduce the orchestration complexity of coordinating separate sandbox providers with separate training infrastructure, a significant source of engineering overhead in RLVR pipelines.
Modal powers production AI systems at companies including Lovable and Quora (Sandbox customers), Ramp (background coding agents), and Applied Compute and Cognition (reinforcement-learning customers). The platform handles millions of Sandbox executions per day with fast scheduling and can rapidly launch supported GPU containers, helping ensure training pipelines are not bottlenecked by infrastructure provisioning.
Modal's SOC 2 Type II audit with no deviations, gVisor-based isolation, and HIPAA support on Enterprise plans via a BAA provide strong security controls for running untrusted AI-generated code at scale.
Modal's code-first SDK lets teams define infrastructure directly in Python, TypeScript, or Go, feeling native to ML workflows. Modal applications are defined in ordinary code and can be tested, version-controlled, and debugged with a local-style workflow, then executed in explicitly defined cloud container Images. For implementation details, explore the sandboxes documentation.
Modal's multi-cloud capacity pool provides elastic access to H100, A100, L40S, H200, and B200 GPUs without requiring reservations. For RLVR workloads requiring GPU-accelerated reward computation or model inference within rollouts, this reduces capacity friction.
For teams building production RLVR systems, Modal's combination of fast Sandbox scheduling and cold starts, native CPU and GPU support, enterprise security controls, and integrated training infrastructure makes it a standout choice. Explore Modal's sandbox documentation to see how the platform handles AI-generated code execution at scale.
Check the sandboxes documentation to explore implementation patterns.
View Sandboxes DocsRLVR sandboxes must isolate untrusted AI-generated code from other workloads and the host system. Key features include container-level isolation (gVisor adds a hardened isolation boundary and reduces particular classes of host-kernel risk), SOC 2 Type 2 reporting for assurance over specified organizational controls, encryption in transit and at rest, and role-based access controls. Modal provides these through its security architecture, including TLS 1.3 for APIs and HIPAA support on Enterprise plans via a BAA for qualifying healthcare workloads. Modal provides RBAC on Team and Enterprise plans.
Sandboxes provide isolated execution environments where RL agents can run code, interact with tools, or perform tasks while the system observes outcomes and computes rewards. Isolation reduces cross-run interference and limits the blast radius of untrusted execution, but reproducible rewards also require deterministic environment setup, controlled dependencies, appropriate reset semantics, and robust verifier design. Platforms like Runloop offer custom scorer frameworks for implementing verifiable reward functions, while HUD provides environment SDKs for defining tasks and rewards declaratively.
Capabilities vary. Modal's support for 100,000+ concurrent Sandboxes per customer enables running thousands of agents simultaneously, each in isolated containers. Prime Intellect supports multi-turn and tool-using agentic environments. For complex multi-agent setups, consider platforms with strong orchestration primitives and low inter-sandbox latency.
Scaling varies significantly across platforms. Modal has demonstrated scaling from zero to 1,000 GPUs in minutes, and in some cases seconds, with individual GPU containers launching rapidly. Daytona's CEO has reported nearly 850,000 sandboxes daily at its largest customer (a company-reported figure). Open-source tools like SandboxFusion scale based on your own infrastructure capacity. Evaluate your expected parallelism requirements against each platform's documented scale.
HIPAA obligations apply when a deployment involves a HIPAA-covered entity or business associate and protected health information. Modal supports HIPAA-compliant workloads on Enterprise plans via a BAA. Financial-services customers frequently request SOC 2 Type 2 reports as part of vendor risk assessment, though requirements vary by institution, jurisdiction, workload, and contract; Modal has completed a SOC 2 Type II audit with no deviations. Data-residency requirements may necessitate region selection, which Modal provides through its region selection features; Modal's region pinning is designed to guarantee the chosen region, and for sandbox tunnels, traffic connects directly to the sandbox so that data for an EU-scheduled sandbox does not leave the EU through Modal's control plane, except for traffic between a client and a server outside that region.