[HN Gopher] Sleep framework: A new way for LLMs to retain knowle...
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       Sleep framework: A new way for LLMs to retain knowledge in
       continual learning
        
       Author : acossta
       Score  : 56 points
       Date   : 2026-10-28 12:24 UTC (20 hours ago)
        
 (HTM) web link (research.google)
 (TXT) w3m dump (research.google)
        
       | imimayj1337 wrote:
       | I know we just had the carbon announcement which was distinctly
       | confusing, but comparatively I think that this is an
       | exceptionally well-written article. I do believe that Google has
       | some exceptional scientific minds as illustrated by their ability
       | to take highly technical concepts and explain them in an
       | understandable way for the layperson.
       | 
       | As for the technical nature of this, I'm not sure if I've totally
       | understood correctly despite how well it's written, but are they
       | essentially holding back sort of unweighted parameters built in
       | to the model architecture which they can then populate with new
       | context during 'sleep' and then using the techniques outlined in
       | the 'dream' section to reinforce what gets stored in those MoE
       | weights? That to me seems like a solution which only Google is
       | best placed to solve due to their fully integrated AI stack and
       | historic experience with hardware optimisation, but which would
       | be rather hard to do if you are a more resource and compute
       | constrained organisation
        
         | gavinray wrote:
         | > are they essentially holding back sort of unweighted
         | parameters built in to the model architecture which they can
         | then populate with new context during 'sleep' and then using
         | the techniques outlined in the 'dream' section to reinforce...
         | 
         | Sort of. As the model is interacted with, short term "memories"
         | are accumulated in a sort of buffer/cache. During "sleep" these
         | "memories" are given a ranking/score, and the useful bits are
         | integrated into the weights.
         | 
         | They say the weight update can be done by computing a transform
         | where the end state is the old weights with the addition of the
         | useful semantic knowledge contained in memories. Rather than
         | just additively chucking the entire memory concepts into
         | weights. That's pretty neat.
         | 
         | The subtext for the memory architecture diagram sums the whole
         | thing up pretty tidily I think:                 > Input streams
         | from the active phase are stored in short-term memory before
         | entering the sleep cycle, which bifurcates into Consolidation
         | (transferring fragile short-term tokens into expanded long-term
         | parametric memory using low-rank structures and MoE) and
         | Dreaming (autonomous self-improvement via synthetic routing
         | with random experts and RL refinement).
        
       | gopalv wrote:
       | > On-policy distillation: The expanded student model learns
       | token-specific feedback from teacher distributions generated on
       | self-sampled sequences.
       | 
       | The weird thing is to see this based off the KV-Cache in the
       | diagrams.
       | 
       | Completely different from the Databricks RAFT [1].
       | 
       | My team built "Dreamtime" earlier this year [2], which used a
       | different mechanism to distill out learnings across multiple
       | sessions.
       | 
       | This didn't pull from the token layer, but from user nudges on
       | unsatisfactory responses.
       | 
       | This works like a sawtooth wave where we accumulate context,
       | check for dissonances (a user gets to say "It depends" and fix
       | it), compress it back, retire the old one.
       | 
       | The audit requirements are the real problem for customers, where
       | you need to be able to ask "When did this change? and who changed
       | it?" which was hard to answer from the lowest layer of the model
       | unless you can retain lineage to the original sessions.
       | 
       | A model we train can't serve as an accomplice to a user who wants
       | to hide some financial crimes by nudging the agent repeatedly.
       | 
       | [1] - https://arxiv.org/abs/2403.10131
        
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       (page generated 2026-10-29 09:02 UTC)