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Research · Vendor site 2026-10-04
Tokenless finds flaws in reinforcement-learning environments, evals, and graders that models exploit as reward hacking. The Y Combinator-backed team offers tooling and expertise to harden tasks so training signal reflects real capability gains.
Labs training RL agents or running agent evals who need independent review of environment and grader robustness.
Services are engagement-based via contact requests without public monthly pricing on usetokenless.com.
Tokenless helps teams building RL training or agent evals close grader loopholes so measured progress reflects genuine capability rather than exploits.
What it can do
Unknown is different from unavailable. Each fact carries its own evidence.
| Capability | Value | Evidence | Checked |
|---|---|---|---|
| Problem | Site copy says complex RL tasks create opportunities for exploits that make gains look real when models hack graders. | Facts sourced | 2026-10-04 |
| Services | Tokenless provides tooling and expertise to find and fix environment flaws for training and eval teams. | Facts sourced | 2026-10-04 |
| Goal | Homepage states the mission is to end reward hacking in RL environments. | Facts sourced | 2026-10-04 |
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Tokenless finds flaws in reinforcement-learning environments, evals, and graders that models exploit as reward hacking. The Y Combinator-backed team offers tooling and expertise to harden tasks so training signal reflects real capability gains.
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Not confirmed. API access and subscription access may have different terms; consult the linked sources.
Services are engagement-based via contact requests without public monthly pricing on usetokenless.com.
No retained pricing changes yet. A current price alone does not establish a historical trend.
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