Ranked by what each change is worth on the composite — the gap to the next rung of the ladder times the dimension’s weight. Computed from the scores, not written by the model; the model’s sentence under each is the action.
A concrete product noun or clear service category should appear, e.g., "Flaw-detection tooling for RL environments".
Limit abbreviations; spell out terms like reinforcement learning and environments.
Provide a clearer, low-friction action tied to a specific outcome (e.g., "Request a flaw-detection demo").
All three together: 64 → 74. Every dimension one rung up: 74 (grade B). Ceiling is 95.
Built only from facts already on the page — not the company’s words. Shown so the gap between what was written and what could have been written is concrete rather than a number.
Five alternative headlines in five angles, a subhead, a call-to-action line, and a line-by-line edit for each of the five dimensions — written only from facts already on usetokenless.com, gated like a rating (anything with a struck word is dropped, not sold). Delivered on a private page in about a minute.
Five dimensions on one ladder — 5 · 20 · 35 · 50 · 65 · 80 · 95 — weighted clarity 30%, specificity 20%, jargon 20%, proof 15%, call to action 15%. For each: the rung the page matched, the words that drove it, and what would reach the next rung. Full rubric →
The headline begins with the company name and a generic description rather than a concrete product noun or clear category.
Tokenless We find flaws in RL environments and evals and help you fix them.
Next rungA concrete product noun or clear service category should appear, e.g., "Flaw-detection tooling for RL environments".
Contains a quantified outcome in the blog section and describes a measurable result, even though it's not strictly a product claim on the homepage.
Introducing EnvCheck Across Terminal-Bench 4.0, DeepSWE 1.1, and BFCL v4, our agent swarm found 110 reward hacks and grading bugs.
Anchor matched: uses domain shorthand and abstract terms (RL, evals) without fully concrete product nouns.
RL environments and evals
Next rungLimit abbreviations; spell out terms like reinforcement learning and environments.
No named customers or client metrics, but a numeric claim appears in the blog post; there is no on-page customer proof.
110 reward hacks and grading bugs
There is a contact prompt inviting engagement, which aligns with a basic next step after a claim.
contact us .
Next rungProvide a clearer, low-friction action tied to a specific outcome (e.g., "Request a flaw-detection demo").
Tokenless We find flaws in RL environments and evals and help you fix them.Shows the initial service description but no concrete product category.
found 110 reward hacks and grading bugs.Numeric outcome tied to a measurable result.
Tokenless We find flaws in RL environments and evals and help you fix them. As models become more capable, training and evaluating them requires increasingly complex tasks and longer interactions. This creates more opportunities for unexpected behavior and exploits in tasks and graders. We provide the tooling and expertise to understand and prevent these failures. Our goal is to end reward hacking. Training can amplify small flaws in an environment as models learn to exploit them. These flaws can also make it difficult to distinguish real capability gains from better exploitation. Correcting them makes training more effective, makes model performance easier to interpret, and is crucial to solving the AI alignment problem. If you’re training models with RL, building environments, or running agent evals, and want more confidence in your environments, contact us . Blog Introducing EnvCheck Across Terminal-Bench 4.0, DeepSWE 1.1, and BFCL v4, our agent swarm found 110 reward hacks and grading bugs. Sep 30, 2026
Strongest: Specificity 95. Weakest: Clarity 20. The 5 passes agreed within 17 points.
No word in the headline itself was judged empty, yet jargon-free scores 65: the scorer found generic phrasing further down the page. Anchor matched: uses domain shorthand and abstract terms (RL, evals) without fully concrete product nouns.
Badges are issued to grades A and B. The share card for this page shows the headline with its jargon struck through; paste the URL anywhere that unfurls links.
Scored from the live homepage
on 2026-10-12 by gpt-5-nano,
5 independent passes, median taken.
Page snapshot c272ad71a421.
Rubric 5abbca9a (current; what it says).
No person reviewed or edited this score. If the rubric and the score disagree, the rubric wins
and the page is re-scored — not hand-corrected.
Re-scored when the homepage text changes.