Tarun Reddi @_TeenDifferent
Founder Scoleaf | AI Researcher 🤖 | Writer bento.me/tarunreddi Joined December 2019-
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I think Sutton is right about basically everything Imitation in animals isn't imitation. It is a goal to imitate, and the *motor actions* are learned without any data. The starling cannot imitate the motor actions, it doesn't see it. It is a goal to imitate
Great blogpost walking through tokenization vs "tokenize free" approaches, arguing that there isn't really such thing as "tokenize free" and even using utf8 bytes inherits choices made by other people (Unicode consortium) and is not clear these are sensible for LLMs.
Great blogpost walking through tokenization vs "tokenize free" approaches, arguing that there isn't really such thing as "tokenize free" and even using utf8 bytes inherits choices made by other people (Unicode consortium) and is not clear these are sensible for LLMs. https://t.co/2JaTSWuRmI
Mako is hiring GPU kernel engineers. If you know how to write #CUDA, #Triton, or #HIP, apply and help us solve some of the hardest problems in code generation. We have the tools. We have the means. Algorithm discovery and development will never be the same. Join @mako_dev_ai!
RLPT: Reinforcement Learning on Pre-Training Data • RL directly on pre-train data (no human labels) • Next-segment reasoning objective (ASR + MSR tasks) → self-supervised rewards • Gains on Qwen3-4B: +3.0 MMLU, +8.1 GPQA-Diamond, +6.6 AIME24, +5.3 AIME25
The Impact of Initialization on LoRA Finetuning Dynamics a clean bit of scientific exploration and one of the better papers i've read all year. (and yes i am pretty much LoRA-pilled) in Low-Rank Adaptation, we typically train adapters A and B that are added to weights as W +…
seems likely the world of One Big Model will end in a year or two we’ll have families of peft-adapted experts continuously retrained, merged, and reapplied under varying degrees of staleness the Train/Test split of conventional machine learning held us back for far too long
SGLang now supports deterministic LLM inference! Building on @thinkymachines batch-invariant kernels, we integrated deterministic attention & sampling ops into a high-throughput engine - fully compatible with chunked prefill, CUDA graphs, radix cache, and non-greedy sampling. ✅…
This is really cool - you can try it out by signing into chat.qwen.ai (with Google or GitHub) and selecting the audio icon The model weights are only ~70GB and that's before quantizing them down, so this one is going to be reasonably accessible to run locally
This is really cool - you can try it out by signing into chat.qwen.ai (with Google or GitHub) and selecting the audio icon The model weights are only ~70GB and that's before quantizing them down, so this one is going to be reasonably accessible to run locally
We present a new approach to time-series forecasting that uses continued pre-training to teach a model to adapt to in-context examples at inference time, matching the performance of supervised fine-tuning without additional complex training. Learn more at goo.gle/3Vwpp6F
Some resources about GPUs I found good as a noob in GPU programming, 1. jax-ml.github.io/scaling-book/g… 2. modal.com/gpu-glossary/p… 3. multimodalai.substack.com/p/the-mlai-eng… 4. bytesofintelligence.substack.com/p/maximizing-g… 5. youtube.com/playlist?list=…
gotta update my bios lol they’re kinda stale… currently building something super cool w/ a ridiculously fun team can’t wait to share soon 👀
this is actually exciting…i’m not really a kernel ops person, just started working into recently… but turns out kernel operations can literally affect llm inference behavior ….is crazy. their batch-invariant kernel fix is super clean… Blog link 🧵 thinkingmachines.ai/blog/defeating…
Smtyms it ain’t bout hardwrk… it’s about time, fails, tweaks, n learnin on the fly … but it ain’t wrkin rn… let’s see 🥲
it’s been an interesting ride watching the conventional nomenclature of machine learning gradually lose all meaning. there used to be TRAIN and TEST and everything was simple. now we train on the universe. and we test on the universe, too. are we gaming our benchmarks? are we…
🚨 We’ve just published a recipe to train a frontier-level deep research agent using RL. With just 30 hours on an H200, any developer can now beat Sonnet-4 on DeepResearch Bench using open-source tools. (Thread 🧵)
Some late night 📸
New in-depth blog post - "Inside vLLM: Anatomy of a High-Throughput LLM Inference System". Probably the most in depth explanation of how LLM inference engines and vLLM in particular work! Took me a while to get this level of understanding of the codebase and then to write up…
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