pith:3AKEZE3T
MemAgent: Reshaping Long-Context LLM with Multi-Conv RL-based Memory Agent
MemAgent lets LLMs handle millions of tokens by segmenting input and overwriting memory after RL training on 32K texts.
arxiv:2507.02259 v1 · 2025-07-03 · cs.CL · cs.AI · cs.LG
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Claims
MemAgent has demonstrated superb long-context capabilities, being able to extrapolate from an 8K context trained on 32K text to a 3.5M QA task with performance loss < 5% and achieves 95%+ in 512K RULER test.
That the overwrite memory strategy combined with multi-conversation RL training will continue to prevent performance degradation when scaling far beyond the 32K training length without additional mechanisms or data.
MemAgent uses multi-conversation RL to train a memory agent that reads text in segments and overwrites memory, extrapolating from 8K training to 3.5M token QA with under 5% loss and 95%+ on 512K RULER.
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| First computed | 2026-05-17T23:38:52.698861Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
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· · · · ·Agent API
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Canonical record JSON
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