Memento: Teaching LLMs to Manage Their Own Context
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New research from Microsoft teaches reasoning models to compress their own chain-of-thought mid-generation. Memento trains models to segment reasoning into blocks, summarize each block into a compact “memento,” and then evict the original block from the KV cache. The model continues reasoning from mementos alone, cutting peak memory by 2-3x while nearly doubling throughput.
Block-and-compress architecture: The model learns to mark reasoning boundaries using special tokens, produce a terse summary capturing key conclusions and intermediate values, and then drop the full block from context. From that point forward, the model sees only past mementos plus the current active block, keeping context compact without losing critical information.
KV cache reduction with minimal accuracy loss: Applied to five models including Qwen2.5-7B, Qwen3 8B/32B, Phi-4 Reasoning 14B, and OLMo3-7B-Think, Memento achieves 2-3x peak KV cache reduction with small accuracy gaps that shrink at larger scales. The erased blocks still leave useful traces in the KV cache that the model leverages.
Practical throughput gains: Beyond memory savings, the reduced context length directly translates to faster inference. The approach nearly doubles serving throughput, making it immediately useful for production deployments where both latency and memory are constraints.
Open resources: Microsoft released the full codebase under MIT license, the OpenMementos dataset containing 228K reasoning traces with block segmentation and compressed summaries, and a custom vLLM fork for KV cache block masking. Standard supervised fine-tuning on approximately 30K examples is sufficient to teach this capability.
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