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Sasha Rush prototypes Lean-verified Rust tokenizer for agentic RL training

1 article1 sourcesince Oct 27Last article

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Sasha Rush has prototyped a Rust tokenizer and agent pipeline whose key properties are formally verified in Lean, according to RadixArk, which praised the work on X. The prototype addresses training-inference mismatch in reinforcement learning by verifying the tokenizer pipeline itself, rather than relying on chat messages built with Jinja templates.

RadixArk says the approach extends Miles' token-in-token-out idea, which tests that training sees the exact tokens the model generated, into formal verification. The source reports no benchmarks or release date for the prototype.

Written by AI from the articles below · updated Oct 27, 8:06 PM ET

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Latest developmentOct 27, 8:04 PM ET
RadixArk praises Sasha's Lean-verified Rust tokenizer for agentic RL

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The articles in this story. Times are ET.

Oct 27
  1. RadixArkOfficial
    RadixArk praises Sasha's Lean-verified Rust tokenizer for agentic RL

    AIRadixArk praises Sasha's work formally verifying properties of a Rust tokenizer in Lean, which extends Miles' token-in-token-out idea beyond testing that training sees the exact tokens the model generated. Sasha's prototype, posted by Sasha Rush (@srush_nlp), addresses training-inference mismatch in RL by verifying the tokenizer pipeline rather than relying on messages built in Jinja.

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