Edit ‘autogollark’

autogollark.myco

@@ -22,22 +22,23 @@ Autogollark currently comprises the dataset, the search API server and the [[htt
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 * {Tool capabilities (how to get the data? Examples in context only?!).
 * Synthetic via instruct model.
-* {RL (also include reasoning, of course). Probably hard though (sparse rewards). https://arxiv.org/abs/2403.09629. [[https://arxiv.org/abs/2503.22828]] would probably work. [[https://arxiv.org/abs/2505.15778]] [[https://arxiv.org/abs/2505.24864]] [[https://arxiv.org/abs/2509.06160]]
+* {RL (also include reasoning, of course). Probably hard though (sparse rewards). [[https://arxiv.org/abs/2403.09629]] (bad?). [[https://arxiv.org/abs/2503.22828]] would probably work. [[https://arxiv.org/abs/2505.15778]] [[https://arxiv.org/abs/2505.24864]] [[https://arxiv.org/abs/2509.06160]]
 * Unclear whether model could feasibly learn tool use "from scratch", so still need SFT pipeline.
 }
 * https://arxiv.org/abs/2310.04363 can improve sampling (roughly) //and// train for tool use. However, it seems really annoying.
 }
 * {Local finetune only? Would be more tonally consistent but dumber, I think.
 * Temporary bursts of hypercompetence enabled by powerful base model are a key feature. Small model is really repetitive.
-* Can additionally finetune on "interesting" blog posts etc (ref https://x.com/QiaochuYuan/status/1913382597381767471).
+* Can additionally finetune on "interesting" blog posts etc (ref https://x.com/QiaochuYuan/status/1913382597381767471). Maghammer archival data, books, transcripts.
 * Decision theory training data (synthetic, probably) (ref https://arxiv.org/abs/2411.10588).
-* ~~Pending:~~ Resource now available: XEROGRAPHIC BIFROST 3.
+* ~~Pending:~~ Resource now available: [[XEROGRAPHIC BIFROST]] phase 3.
 * https://arxiv.org/abs/2507.07101
 * https://arxiv.org/abs/2507.01335
+* https://github.com/d0rc/egg.c and https://eshyperscale.github.io/. Does this actually work? Why?
 }
 * MCTS over conversations with non-gollark simulacra? Should find //something// to use spare parallelism on local inference. Best-of-n? https://arxiv.org/abs/2505.10475
 * {Longer context, mux several channels.
-* {No obvious reason Autogollark can't train (and run inference!) on every channel simultaneously, with messages sorted by time and other non-Discord things (tool calls?) inline. Not good use of parallelism but does neatly solve the when-to-respond thing.
+* {No obvious reason Autogollark can't train (and run inference!) on every channel simultaneously, with messages sorted by time and other non-Discord things (tool calls?) inline. Not good use of parallelism but does neatly solve the when-to-respond thing. Maybe we can process channels in parallel and fudge the K/V caches.
 * Context length issues, and subquadratic models are sort of bad, though maybe we can "upcycle" a midsized model to RWKV. This exists somewhere. Not sure of efficiency. Inference code will be awful.
 }
 }