The Synthetic Data Company

Long-horizon training environments

Verified EnvironmentsForCoding AgentsAnd Post-Training Loops

The Synthetic Data Company builds and sells verifiable training environments for AI labs running RL and post-training. Our 1,000+ environments across 30+ domains pair binary rewards with rich feedback signals: detailed test failures, compiler diagnostics, LLM critique, debugging traces, and annotated-image review for stronger long-horizon agent performance.

1,000+

Verifiable environments

30+

Domain categories

4

Environment types

The Problem

Labs Are Environment-Constrained, Not Algorithm-Constrained

Every serious lab can build an RL loop, but very few can build enough production-quality post-training environments fast enough to keep model improvement compounding.

Building one production-quality environment with calibrated milestones, parameterized difficulty, and robust verifiers can consume weeks of senior domain effort. Post-training pipelines need hundreds.

The Scaling Law

More Environments Produce Better Models

Model performance scales log-linearly with the number of diverse, verifiable long-horizon training environments, so environment count directly controls downstream capability growth.

Practical implication

Performance ~= base capability + slope * log(environment count)

This is why synthetic data for AI agents must be diverse, verifiable, and continuously expanding.

Environment Types

Four Verifiable Environment Families

The catalog spans coding environments, ML and AI environments, gaming environments, and math or formal verification environments so training loops can diversify behavior while keeping reward quality high.

Coding Environments
Coding environments train coding agents on real software projects with verifiable rewards from open-source test suites, so agents must architect, implement, debug, and iterate across long-horizon tasks.

C compiler with four-tier suite (basics -> wacct -> c-testsuite -> GCC torture)

AWS service clones (S3, DynamoDB, SQS, Lambda-compatible runtime)

Mattermost monolith feature development with blast-radius checks

ML & AI Environments
ML and AI environments evaluate synthetic data for AI agents by training downstream models and scoring held-out performance, so reward signals are scalar metrics like accuracy, F1, and BLEU.

Synthetic data generation loops for downstream benchmark lift

Hyperparameter optimization with budget-constrained training

Model artifact generation scored by actual retraining

Gaming Environments
Gaming environments stress long-horizon planning and policy quality by requiring agents to improve against progressively stronger opponents or target scores.

Tetris, NetHack, Chess, Go, and Poker progression tracks

Pokemon Red and Civilization strategy environments

Milestones that demand 3-5x performance jumps

Math & Formal Verification
Math and formal verification environments use machine-checked proofs as the oracle, giving binary correctness for Lean 4 tactics with no subjective grading.

Compiler IR verification

Type system soundness proofs

Cryptographic protocol correctness proofs

The Full Catalog

30+ Domains Of Long-Horizon Training Environments

This catalog is the moat: each domain contains difficult, production-scale tasks with verifiable rewards and structured milestones for RL training environments.

Systems Programming

78

Systems programming environments train agents to build foundational runtimes and developer tools from first principles.

Build a C compiler that passes GCC torture tests

Implement SQLite and PostgreSQL wire protocol compatibility

Recreate Git internals, regex engines, and interpreters

Cloud Infrastructure

64

Cloud infrastructure environments evaluate whether agents can ship resilient distributed services with strict interfaces.

S3-compatible object store

DynamoDB clone with partitioning and consistency tests

Kubernetes-style orchestrator and API gateway

Compilers & Language Tools

61

Compiler environments verify parsing, typing, optimization, and runtime semantics with staged milestone curricula.

Verilog compiler

TypeScript type checker

CSS layout engine and GraphQL execution engine

Networking & Telecom

47

Networking environments benchmark protocol correctness, throughput, and failure handling in realistic transport stacks.

TCP/IP stack from scratch

DNS resolver and SSH implementation

QUIC transport and 5G core network simulator

Scientific Computing

40

Scientific environments measure numerical correctness and stability across large-scale simulation workflows.

OpenFOAM CFD solver tasks

LAMMPS molecular dynamics pipelines

Finite element and Navier-Stokes solvers

ML Infrastructure

54

ML infrastructure environments test whether coding agents can optimize core model serving and training components.

ONNX runtime implementation

Tokenizer and autograd framework

KV-cache inference server with continuous batching

Game Engines & Emulators

45

Engine and emulator environments verify low-level correctness under demanding compatibility suites.

Game Boy, NES, SNES, and Sega Genesis emulators

RISC-V emulator

Physics engine and ECS framework

Media & Creative Tools

38

Media environments evaluate rendering, encoding, and layout fidelity against real artifact checks.

PDF renderer and SVG engine

H.264, AV1, and VP9 codec tasks

Font rasterizer and path tracer

Finance

34

Finance environments test precision-critical implementations with strict correctness and latency goals.

Options pricer

Order book matching engine

Basel III risk engine and actuarial reserving

Healthcare & Biology

30

Healthcare and biology environments validate parsers, detectors, and simulation tasks with domain-specific constraints.

DICOM image reader

HL7 FHIR parser

ECG QRS detector and molecular docking

Storage Engines

42

Storage environments train reliable data systems with verifiable durability, correctness, and recovery behavior.

B-Tree and LSM-Tree key-value stores

Raft consensus and write-ahead logging

Redis clone with replication tests

Cryptography

36

Cryptography environments enforce exact algorithmic correctness and protocol compliance.

AES across all major modes

SHA/BLAKE3 and Argon2

X.509 parser and Groth16 prover

Web Platform

33

Web platform environments test browser-grade standards conformance and protocol interoperability.

HTTP/2 server and TLS 1.3

DOM implementation and CSS parser

Chromium Web Platform Tests compatibility targets

Formal Verification

23

Formal verification environments provide binary reward signals from theorem checkers rather than subjective judgment.

23 Lean 4 theorem-proving environments

Separation logic and type systems

Compiler and cryptography proof tracks

Game Agents (Turn-Based)

31

Turn-based game environments isolate planning and policy quality with crisp objective scoring.

Tetris, NetHack, Chess, and Go

Poker and Sokoban

Pokemon Red and Civilization

Enterprise Deployment

29

Enterprise environments test agent reliability when changing live-style monolith and microservice systems.

Mattermost feature development

E-commerce monolith and microservices variants

Chaos engineering and regression blast-radius checks

Aerospace

19

Aerospace environments benchmark precision numerical workflows and domain simulation correctness.

NASA GMAT workflows

Nektar++ simulations

Trajectory optimization tasks

Agriculture

18

Agriculture environments evaluate planning and simulation under agronomic constraints.

APSIM pipeline integration

DSSAT scenario optimization

Yield and water-stress forecasting

Performance Codecs

24

Performance codec environments stress compiler-level and kernel-level optimization skills.

GEMM micro-kernel optimization

Sparse matrix kernels

SIMD pipeline tuning

Filesystems

22

Filesystem environments validate persistence guarantees and compatibility behavior.

ext4 and FAT32 tasks

FUSE user-space filesystem implementations

Consistency and crash-recovery suites

Robotics & Controls

21

Robotics environments test control loop reasoning and planner quality with verifiable outcomes.

Model predictive control implementations

Path planning with safety constraints

Closed-loop simulator integration

Geospatial & Mapping

22

Geospatial environments evaluate data pipelines and spatial algorithms on production-like maps.

Tile server and vector pipeline

Route planner with constraints

Large-scale geocoding and indexing

Cybersecurity

27

Cybersecurity environments benchmark detection, hardening, and protocol correctness in adversarial settings.

Static and dynamic analysis pipelines

Exploit mitigation verification

Network hardening automation

Mobile Platforms

20

Mobile environments test performance-sensitive product delivery with compatibility and battery constraints.

Cross-platform runtime features

Rendering and memory optimization

Offline sync correctness

Embedded Systems

28

Embedded environments require low-level correctness and hardware-aware implementation decisions.

RTOS component implementations

Device driver bring-up

Firmware update reliability checks

Data Engineering

44

Data engineering environments validate ingestion, transformation, and reliability at production scale.

Batch and streaming pipeline orchestration

Schema evolution handling

Backfill correctness and lineage tracking

Observability & SRE

31

Observability environments test incident response, monitoring instrumentation, and failure diagnosis.

Distributed tracing pipelines

SLO burn-rate alerting

Root-cause triage environments

Distributed Systems

39

Distributed systems environments benchmark consensus, replication, and fault tolerance under stress.

Event-sourced architectures

Replication lag and failover scenarios

Consistency model verification

DevTools & CI

37

DevTools environments train agents to optimize software delivery loops and quality gates.

Build system and cache implementations

CI pipeline scheduling and flaky-test handling

Static analysis + autofix tooling

Productivity Software

24

Productivity environments test end-to-end feature delivery in user-facing application surfaces.

Collaborative editing features

Permission model implementations

Search and indexing quality

Legal & Compliance

18

Compliance environments verify policy implementation and evidence generation for regulated workflows.

Audit-log correctness

Policy evaluation engines

Data retention and deletion guarantees

Education & Tutoring

17

Education environments evaluate scaffolded curriculum generation and adaptive feedback loops.

Adaptive lesson sequencing

Assessment generation with formal checks

Feedback quality evaluation pipelines

Why Ours Are Different

What Makes These Environments Different From Benchmarks?

These environments are built for post-training environments where verifiable rewards and long-horizon behavior matter more than short benchmark hacks.

Long-Horizon

These environments are multi-day and multi-milestone projects, so agents must architect, build, debug, refactor, and sustain effort across hours to weeks.

Verifiable

Every task is judged by real test suites and executable oracles with binary ground truth, not LLM-as-judge scoring.

Structured

Milestones decompose each project into a natural curriculum so training loops can progress from fundamentals to advanced phases.

Parameterized

Each environment exposes configuration knobs such as target architecture, implementation language, and milestone scope to generate many training variants.

Production-Scale

These are internet-scale systems and formal reasoning tasks that reflect how senior engineers and researchers actually work.

How It Works

How Do You Integrate These Environments?

Integration is simple: pick environments, run your agent, and collect verifiable trajectories for your training loop.

Step 1

Pick

Pick long-horizon training environments from the catalog based on your post-training roadmap and domain gaps.

Step 2

Run

Run your agent harness against the environments using Claude Code, Codex, OpenCode, or internal infrastructure.

Step 3

Collect

Collect structured traces and verifiable rewards for SFT, GRPO, DAPO, DPO, or any custom reinforcement learning loop.

Who It Is For

Who Uses These RL Training Environments?

We serve teams that need synthetic data for AI agents with hard, verifiable long-horizon tasks.

AI Labs

AI labs use our post-training environments to scale RL and SFT on coding agent training, game-playing agents, and formal reasoning agents.

Agent Developers

Agent teams benchmark and improve reliability by running realistic long-horizon tasks instead of toy benchmarks.

Research Groups

Research groups study long-horizon decision making, reward shaping, and generalization with rich, verifiable telemetry.

Get Started

Start Training On Verifiable Long-Horizon Tasks

Request access to the catalog or request a custom environment for your post-training roadmap.