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In Sept 2024, o1 surprised many purists who thought inference-time scaling for LLMs was through MCTS.
What if a connection exists, just implicit? What does it imply?
New post: "Squint enough and RLing CoT reasoners is approximable as Monte Carlo Tree Search policy learning." 🧵
Hiranmay Darshane retweeted
Fantastic evening for our first Paper Club meeting in Mumbai!
Lovely meeting everyone and diving into the latest research on continual learning
Thank you Dr. Shweta for hosting us at the Homi Bhabha Centre for Science Education
Happy Diwali to all who celebrate 🪔
Hiranmay Darshane retweeted
New blog!
This one covers my experiments on fine-grained credit assignment with an agentic judge, and how it affects the models' performance and reasoning quality and some thoughts on RL science.
Link below, please do check it out, and RT/QT if you like it:)
Hiranmay Darshane retweeted
This report shares our three subjective conclusions about risk:
1. We believe that, as of Oct 2026, the pace of capability growth on well-specified tasks is likely about 2x what it was from Mar 2024 to Oct 2025. We’re highly uncertain and would not be surprised by 1.3x–3x.
not sure why they want a full-on pause when they can just nationalise + accelerate internally. it's effectively a pause since proliferation stops then and there. like you don't have to give up the lead + the possibility of increasing it. IG depends on whether you want pause because you believe in "loss of control" etc. (nationalisation doesn't do anything to that) vs to dampen the rate of change (limited proliferation def does that)
was speculating about something similar in shape some time back with a friend. obviously my thoughts were much less concrete, but cool to realise we're heading into a regime where this is totally plausible (crazily enough, even at direct weights-level).
one of the things I’m most interested à la PRH is the eventual idea that we can coldstart initialize new LM architectures quickly via known existing trained models + use LLMs to fill in the weights of arbitrary new model shapes based on our findings here, rather than pre-train over the same corpus mixtures thousands of times
it seems like a superhuman problem that’s far away, but a natural starting point is understanding how well LLMs understand and edit the weight space of DL models.
in the first few months of starting at MIT, @rikiyatakehi @jayden_teoh_ were able to find some very interesting results here :)
feeling spicy: how continual learning gets solved is a hidden-in-sight race between when models get good enough to manually update themselves at weight or ctx level (direct edits on KV cache, engram modules, etc.) vs when humans find the correct recipe (unlikelier by the day)
Tick tock
Its crazy man
How is any of this even real
Lol
We asked GPT-6 Astra to edit Qwen3.5-0.8B weights to improve math.
It directly changed only 0.016% of weights, fixing 0.8B's reasoning loops & improving math up to 6x and even instruction following 3x, without gradients or training.
We call this Weight-Space Programming: frontier LLMs can now natively read/write neural network weights! 🧵
Blog: rikiyat.github.io/blog/weigh…
Readers added context they thought people might want to know
LLM was given weights explicitly as text in prompt and used code/forward passes (per authors' logs) to ablate Qwen looping direction, a known thinking-mode issue per model card. Primarily fixed repetition in that mode, not general 'natively' improving weights/math.
rikiyat.github.io/blog/weight-sp…
github.com/RikiyaT/lm_und…
huggingface.co/Qwen/Qwen3.5-0…
huggingface.co/RikiyaT/Qwen3.…
out with all CL talk talks about what we do to the learner, in with all the CL talk that talks about what the learner does to itself
Many trukes
My views on AI have radically shifted over the past few weeks, after spending way too much time reading about it and watching interviews of industry leaders and experts.
First, I really love the tools. I spend $200 on Claude every month and the product is worth every penny for me. Probably even more. I think the existing tools can deliver significant increase to future GDP and help us solve major global challenges faced by humanity.
However, I just cannot comprehend why the industry is not much more regulated, as its leaders and employees seem to request themselves. I meet a lot of people who have heard of the hugging face incident but don’t know the actual chain of events. If you don’t, you definitely should spend a least an hour reading about it.
I also do not know why we need to follow the path that frontier labs are following, to develop ever more powerful models. It seems to me that they are enormous gains that are readily available if we deploy the current technology in economic and scientific organizations without pursuing a super intelligence that they say might kill us and more likely wreak havoc in society. I know a lot of people think these warnings are marketing tricks, and that’s a reasonable assumption. But I don’t see the point of taking such risks.
My median scenario is that of many economists: AI will diffuse slowly and lift gdp growth by a percentage point or so, will cause some economic disruption on the way, but ultimately people will get new jobs and maybe work less.
However, I am deeply uncomfortable with the absolutely wild level of uncertainty around this prediction. And to be honest, even setting the doom scenarios aside, I find the utopia scenarios of some industry leaders actually dystopian. And I think a large majority of the public would agree if they paid attention to what they’re saying. To their credit, they themselves agree publicly that they should not be the ones making these choices. A large majority of the public is now very skeptical of AI as a source of human progress, in particular among younger adults who use it the most. They should have a say.
I don’t want to convince you of anything but to get informed about this topic, express yourself and vote accordingly.
Hiranmay Darshane retweeted
glimpses of the future
Aware is (basically) a browser we're building to explore personal computing and generative, malleable software. it keeps a footprint of the materials (pages, files) as a graph in a relational database stored locally.
Been writing my paper in my own latex editor inside Aware. It's very satisfying when software fixes itself and fetches its own context and attach them to the right parts.
The editor is an experiment of "interface as a skill": the editor's code and its skill live in the paper's repo, so the same agent that knows my code and data also lives in the editor. So i get:
1. an interface i can reprogram any part anytime (aesthetics i like, and the scratch pad i always wanted)
2. an agent living in the editor, with the whole project as context (a figure links to the dashboard, built from the real analysis code that made it)
(though i have to admit, some of my focused writing time now goes to wanting to tweak the editor instead)
All the thonk-bois high on their own thought, writing forecasts (often super negative and dystopian ones), must remember, however logically plausible or agreeable those forecasts may be, that these are times when even writing should have empathy for the masses and a general texture of sobriety.
A doctor can’t say “hey, you have cancer” and just walk out of the room. Nobody’s asking the doctor to lie about the diagnosis. If they believe it’s true, say it, and say it plainly. Then stay in the room, put a hand on the shoulder, and talk about what can be done next.
Writing “displacement is coming, everyone will lose their jobs, but this must happen anyway, the line must go up, it’s some great fundamental law of nature” is like the doctor delivering the diagnosis, adding that treatment is pointless because it’s all the natural order of things, and then leaving. Dressing up inevitability as wisdom (thinking it's some sigma alpha thonk-boi hardass moment) is just corrosive and distasteful.
Be as bleak as the evidence warrants (or as much as you want to be). But these are times when one should write like the people most affected in your forecasts are reading it, because they probably are, and doom-prophecies just by themselves help very little (only reflect poorly on you).



















