@b_geist: RL for continuous learning is slow and can lead to catastrophic forgetting; OPSD does not work reliably. At Ramp Labs w…

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Summary

The blog post introduces a multi-part series on techniques for efficient continuous learning in AI, exploring alternatives to reinforcement learning and using KV cache memory at Ramp Labs.

RL for continuous learning is slow and can lead to catastrophic forgetting; OPSD does not work reliably. At Ramp Labs we’ve been taking an alternative bet to continuous learning, one that involves efficient accumulated memory in the KV cache over changing parametric knowledge. This blog is the start of a multi part series on techniques my coworker Jeff and I have been exploring over the last few months to bring these techniques to the frontier. Watch this space 👀
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RL for continuous learning is slow and can lead to catastrophic forgetting; OPSD does not work reliably. At Ramp Labs we’ve been taking an alternative bet to continuous learning, one that involves efficient accumulated memory in the KV cache over changing parametric knowledge.

This blog is the start of a multi part series on techniques my coworker Jeff and I have been exploring over the last few months to bring these techniques to the frontier. Watch this space 👀

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@oneill_c: https://x.com/oneill_c/status/2077453217609453784

X AI KOLs Timeline

A researcher discusses the challenge of continual learning in LLMs, comparing them to amnesiac interns, and explores approaches like extending context windows, building stateful memory, and compressing context into latent representations, citing their work on Still.