Production-Grade RL Environments for AI Agents
Deaimer designs reproducible, verifiable environments and evaluation tasksets for training and testing coding agents. Our work covers terminal operations, software engineering, DevOps, system administration, debugging, scientific computing, security, and long-horizon workflows.
Environments built to hold up under scrutiny.
Containerized, resettable environments
Task environments that spin up clean and reset deterministically between runs.
Realistic terminal & repo workflows
Terminal operations and repository-level tasks that mirror real engineering work.
Automated graders & reward signals
Hidden tests, graders, and reward functions designed for verifiable outcomes.
Reference solutions & golden trajectories
Vetted solution paths that anchor grading and downstream training.
Oracle & negative-control validation
Every task validated against a working solution and known-failing baselines.
Reproducibility & dependency checks
Environment and dependency verification so tasks run the same way every time.
Shortcut, leakage & reward-hacking analysis
Adversarial review for shortcuts, data leakage, and reward-hacking exposure.
Model-run & trajectory evaluation
Repeated model trials with trajectory-level review of how agents actually solve tasks.
Third-party work across major benchmarks.
Through third-party engagements, Deaimer's teams have produced and reviewed coding-agent tasks associated with Terminal-Bench 2.0, 2.1 and 3.0, Terminal-Bench Science, and SWE-bench-style evaluations. Our experience includes environment construction, task authoring, verifier development, reference solutions, model-run analysis, and independent quality assurance.
Deaimer is an independent contributor to select third-party benchmark programs. Deaimer does not own, operate, sponsor, or hold an official partnership with Terminal-Bench or SWE-bench, and does not display their marks without permission.
Author and reviewer separated by design.
Deaimer operates a supervised in-office coding team, supported by a network of 400+ computer-science, AI, mathematics, physics, and engineering specialists. Every task passes through separate author and reviewer workflows before it is accepted.
Scope & capability definition
Define the agent capabilities and task categories the environment needs to cover.
Task & environment construction
Build the containerized environment, task setup, and supporting assets.
Oracle & verifier validation
Confirm the task is solvable and correctly graded via a working reference solution.
Independent adversarial review
A separate reviewer probes for shortcuts, leakage, ambiguity, and reward hacking.
Repeated model trials
Multiple model runs to confirm difficulty, stability, and signal quality.
Versioned delivery & evidence report
Delivery with version history and a report documenting how each task was validated.
Where these environments get used.
Coding-agent post-training
Training environments and tasksets for post-training coding agents.
RLVR & reinforcement learning
Verifiable-reward environments built for RLVR and RL training pipelines.
Private model evaluations
Confidential evaluation suites for internal model benchmarking.
Benchmark development
End-to-end construction of new coding-agent benchmarks.
Agent safety & robustness testing
Adversarial tasks that probe agent behavior at the edges of its capability.
Dataset & environment expansion
Scaling an existing task suite into new domains and difficulty tiers.
Start with a controlled pilot.
Deaimer can produce or independently audit a small batch of coding-agent tasks under your specifications, security requirements, and acceptance criteria.