FIX YOUR CRITICAL PATH BOTTLENECK

Your RL environments take seconds to reset.
Your competitors wait
milliseconds.

Every environment reset is compute burned. Every checkpoint reload is CPU time wasted. Profiling shows that environment setup time can account up to nearly 80% of CPU utilization. In multi-turn Reinforcement Learning, the high cost of environment management is one of the leading causes of ballooning CPU spend and GPU idle time for synchronous RL frameworks.

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1000x

Faster Environment Reset

∞

Parallel Rollouts

<2MB

Memory Overhead per Fork

17ms

Fork Latency

Multi-turn RL is broken.
This is already known.

Complex environments. Stateful simulations. Multi-agent dynamics. Every reset from scratch. Every exploration path serialized. Your CPUs spend more cycles arriving at a state than they do performing work.

⏰

Multi-second Resets

Complex code environments, multi-agent worlds, and robotics simulations take long to initialize. You're paying for GPU time while wasting CPU resources to initialize glibc's heap for the millionth time.

💾

Checkpoint Hell

Save states balloon to gigabytes. Loading them takes minutes. Exploring multiple branches means exponential storage costs.

🔄

Serial Exploration

Want to try 100 different actions from the same state? That's 100 sequential reloads. The competitions is already at iteration 1000.

Instant. Parallel. Efficient.

Fork entire RL environments in milliseconds. Zero memory overhead. Massively parallel exploration. This isn't optimization. It's a paradigm shift.

                    # Before: Spin-up nightmare

                    for action in action_space:

                      env.load_and_exec_docker(state, n=100)  # Multiple seconds

                      reward = env.step(action)

                      results.append(reward)

                    # After: Instant parallel exploration

                    envs = zdrl.fork(env, n=1000)  # 17 milliseconds

                    results = parallel_map(lambda e, a: e.step(a), envs, actions)

                    # 1000x faster. Minimal memory footprint. Light config.

Built for impossible workloads.

When your RL environments span gigabytes and your experiments need millions of rollouts, traditional approaches collapse. ZDRL doesn't optimize the old way. It invents a new one.

Microsecond Snapshots

Capture, fork, reset, and reload precise environment states in microseconds, not seconds. Resume from any checkpoint at any depth and as many times as is needed to get the right rollouts fast.

Resource Optimized

Forked environments remain memory efficient even as they diverge, keeping even massive worlds light on footprint.

True Parallelism

Environments materialize at the exact state you care about with almost no downtime, then fan out across massively across instances eliminating environment bootup and rollout reset time.

Low-Touch Integration

Built for code, scientific, monolith, and Python-heavy stacks. Wrap once, keep your code. Quickly wire up proprietary environments that matter.

Deterministic Replay

Perfect reproducibility across forks. Debug that one-in-a-million trajectory.

Cloud Native

Designed for large-scale training fleets with no code rewrites. Easy Kubernetes integration, and ready for the demands of frontier research.

Your RL environments take seconds to reset. Your competitors wait milliseconds.

Multi-turn RL is broken. This is already known.