MemAgent trains an LLM with reinforcement learning to maintain a fixed-size token memory while reading a long document in chunks, and reports near-lossless QA accuracy extrapolating from 32K training context to 3.5M test tokens.
Nextlong: Toward effective long-context training without long documents
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The survey organizes the shift of LLMs toward deliberate System 2 reasoning, covering model construction techniques, performance on math and coding benchmarks, and future research directions.
citing papers explorer
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MemAgent: Reshaping Long-Context LLM with Multi-Conv RL-based Memory Agent
MemAgent trains an LLM with reinforcement learning to maintain a fixed-size token memory while reading a long document in chunks, and reports near-lossless QA accuracy extrapolating from 32K training context to 3.5M test tokens.
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From System 1 to System 2: A Survey of Reasoning Large Language Models
The survey organizes the shift of LLMs toward deliberate System 2 reasoning, covering model construction techniques, performance on math and coding benchmarks, and future research directions.