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AnomalyXL generates synthetic data for long-context time-series RL

Prime Intellect says a 4B TimeRLM trained on fresh AnomalyXL data recursively called sub-agents to locate anomalies.

1 Source, 2h ago, first seen 2h ago

TLDR

Prime Intellect says its RL resident built AnomalyXL, a synthetic generator for long-context time series with programmatic ground truth. The company says a 4B TimeRLM trained with prime-rl on fresh data at every step, using nano-rlm to recursively call sub-agents and localize anomalies across 131k datapoints. It claims the model transfers zero-shot to ECG, sleep and observability data.

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1 Source, first seen 2h ago

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1 Source

Prime Intellect@PrimeIntellectMost open RL environments cluster around the same few domains. Almost none are long-context time series. Prime Intellect RL resident @nzuma0 built one. AnomalyXL is an infinitely scalable synthetic generator with programmatic ground truth, written in verifiers. He then trained a 4B TimeRLM on it with prime-rl, generating fresh data every step, with the model recursively calling sub-agents via nano-rlm to localize anomalies across 131k datapoints. It transfers zero-shot to ECG, sleep and observability data.2h
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    1 Source

    Prime Intellect@PrimeIntellectMost open RL environments cluster around the same few domains. Almost none are long-context time series. Prime Intellect RL resident @nzuma0 built one. AnomalyXL is an infinitely scalable synthetic generator with programmatic ground truth, written in verifiers. He then trained a 4B TimeRLM on it with prime-rl, generating fresh data every step, with the model recursively calling sub-agents via nano-rlm to localize anomalies across 131k datapoints. It transfers zero-shot to ECG, sleep and observability data.2h
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