RL Environment Solutions
Static benchmarks can show whether an agent succeeds. They do not always teach agents how to improve.
Innodata builds custom RL environments for AI agents to train on generalizing across workflows, using tools reliably, recovering from errors, and completing complex, long-horizon tasks.
Realistic training environments that simulate complex agent workflows.
RL gyms that train transferable capabilities, not memorized task paths.
Ground-truth-based evaluation logic built directly into the environment.
Many AI agent evaluation systems depend on LLM-as-judge methods, which can introduce inconsistency, drift, cost, and reward-hacking risk.
Innodata builds RL environments from known ground truth, so agent actions can be scored programmatically and step by step, not guessed after the fact.
This creates more reliable reward signals, clearer failure diagnosis, and scalable evaluation for complex agent training.
Building RL environments requires more than task writing. It requires ground truth, domain expertise, evaluation engineering, reward design, and the operations to scale. Innodata brings these capabilities together in one delivery model.
We have decades of experience creating, structuring, enriching, and validating data for complex AI systems.
Our global expert network supports task design, ground truth development, workflow evaluation, and domain-specific quality review.
We design scoring logic, rubrics, reward functions, and deterministic evaluation methods that turn complex workflows into measurable training signal.
We build tasks backwards from known answers, so agent performance can be evaluated against reliable truth rather than subjective judgment.
We create reward signals that reinforce the capabilities agents need to learn, including reasoning, recovery, planning, tool use, and workflow completion.
We support large-scale task creation, QA, revision, documentation, and delivery for AI programs that need to move beyond one-off benchmarks.
Applications
Browser agent training, computer-use agent training, tool-use evaluation
Customer support, financial analysis, compliance workflows, document workflows
Software development agents, scientific research agents, multi-agent planning
Robotics and embodied AI workflowsRobotics and embodied AI workflows
Identify the task, domain, tools, and desired agent capability.
Build the apps, files, data, personas, states, and constraints.
Define expected outcomes, step-level scoring, and reward logic.
Create long-horizon tasks with realistic complexity and failure conditions.
Train agents, measure performance, diagnose failures, and improve.
Innodata helps frontier AI teams create custom reinforcement learning environments, RL gyms, and agent evaluation systems that train agents for real-world complexity.
Speak with our team about your RL environment needs.
FAQ
An RL gym is a training environment where an AI agent learns by taking actions, receiving rewards, and improving its behavior over time. For AI agents, RL gyms can simulate workflows involving browsers, tools, files, applications, and real-world task conditions.
A benchmark measures performance. An RL environment can also train the agent by providing reward signals throughout the task. Innodata’s environments are designed to support both agent evaluation and agent improvement.
Deterministic evaluation uses known ground truth and programmatic scoring to measure agent performance more consistently. This reduces reliance on judge models and makes reward signals more reliable at scale.
Reward functions define what the agent should learn. Instead of rewarding only the final result, Innodata can create step-level rewards for actions such as finding the right evidence, recovering from an error, using a tool correctly, or completing part of a workflow.
Yes. Innodata can design custom RL environments around specific workflows, domains, applications, tools, difficulty levels, and evaluation criteria.
Depending on the use case, environments can include browsers, mock web apps, files, databases, documents, emails, professional applications, and multi-step enterprise workflows.
Agent improvement can be measured through task success, step-level scoring, capability-level performance, error recovery, tool-use quality, generalization across task variations, and reduced failure patterns over time.
