DeeBee @self_supervised
Building dumb machines with supervised learning; AI researcher in practice | past 》MERL, SamsungResearch | Current 》 Credit and Fraud modeling using AI Joined November 2012-
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LoRA in reinforcement learning (RL) can match full-finetuning performance when done right! 💡 A new @thinkymachines post shows how using 10x larger learning rates, applying LoRA on all layers & more, LoRA at rank=1 even works. We're excited to have collaborated on this blog!
LoRA in reinforcement learning (RL) can match full-finetuning performance when done right! 💡 A new @thinkymachines post shows how using 10x larger learning rates, applying LoRA on all layers & more, LoRA at rank=1 even works. We're excited to have collaborated on this blog! https://t.co/LFVVpv2bUK
I love reasoning. Emergence of reasoning is a necessary and sufficient condition for AGI and ASI. Once pre-training scales to reasoning, we (RL) will take it from there.
What if we could predict customer attrition with high accuracy w/o training on sensitive data? MIT’s "FairRAG" is fast & privacy-preserving. It uses differentially private synthetic data & retrieval-augmented generation — & outperforms traditional ML & LLMs:…
🎉Excited to share that LASeR has been accepted to #NeurIPS2025!☀️ RLHF with a single reward model can be prone to reward-hacking while ensembling multiple RMs is costly and prone to conflicting rewards. ✨LASeR addresses this by using multi-armed bandits to select the most…
🎉Excited to share that LASeR has been accepted to #NeurIPS2025!☀️ RLHF with a single reward model can be prone to reward-hacking while ensembling multiple RMs is costly and prone to conflicting rewards. ✨LASeR addresses this by using multi-armed bandits to select the most…
MIT just built PDDL-Instruct — designed to enhance LLMs’ symbolic planning capabilities. They teach LLMs logical chain-of-thought reasoning for PDDL, covering: ✅ Action applicability 🔄 State transitions 🔍 Plan validity Results: Up to 94% accuracy on benchmarks — a +66%…
Took me 4 days to read this blog, but totally worth it. A great detailed guide to post training. Why aren't many people talking about it. Kudos to @Han_Fang_ @karthikabinav for sharing their thoughts. tokens-for-thoughts.notion.site/post-training-…
Interesting! Hai Huang, @ylecun , and @randall_balestr have just proposed LLM-JEPA. It's the first JEPA-style training framework for language models, bridging the gap between vision’s embedding-space objectives and NLP’s generative ones. Results: - Outperforms standard LLM…
Also strongly recommend this paper on diversity reward in RL! The insights line up closely -- well worth reading together. https:// arxiv.org/abs/2509.15194 (Tencent) https:// arxiv.org/abs/2509.02534 (Meta) Not sure which diversity reward wins out 😀 (embedding vs…
Also strongly recommend this paper on diversity reward in RL! The insights line up closely -- well worth reading together. https:// arxiv.org/abs/2509.15194 (Tencent) https:// arxiv.org/abs/2509.02534 (Meta) Not sure which diversity reward wins out 😀 (embedding vs… https://t.co/HTu1NzJMdz
Excited to share that One-shot RLVR is accepted by @NeurIPSConf 2025, see you all at San Diego!
Excited to share that One-shot RLVR is accepted by @NeurIPSConf 2025, see you all at San Diego!
RL often cause 𝐞𝐧𝐭𝐫𝐨𝐩𝐲 𝐜𝐨𝐥𝐥𝐚𝐩𝐬𝐞: generations become shorter, less diverse, and brittle. A simple fix is 𝐝𝐢𝐯𝐞𝐫𝐬𝐢𝐭𝐲 reward to boost exploration. I use it in many of my projects — surprisingly effective! Details in our NEW paper: arxiv.org/abs/2509.15194
So true, this reminds me of time when we had to ensure multiple runs yield the same output.
So true, this reminds me of time when we had to ensure multiple runs yield the same output.
Why can’t LLMs easily learn from hundreds of in-context examples like humans? 📊🤖 Meet MachineLearningLM—a continued-pretraining framework that gives LLMs strong in-context ML ability without losing general reasoning. Key ideas: - Pretrains on millions of structural causal…
That’s a solid outcome considering there was recent debate in SFT vs RL. It turns out that one compliment the other
That’s a solid outcome considering there was recent debate in SFT vs RL. It turns out that one compliment the other
RL may supersede SFT when its a non-deterministic 👌
RL may supersede SFT when its a non-deterministic 👌
i suspect society was better off with phone call culture than meeting culture
🚨 New paper alert 🚨 Using LLMs as data annotators, you can produce any scientific result you want. We call this **LLM Hacking**. Paper: arxiv.org/pdf/2509.08825
🚀 Excited to share our new survey paper on RL for Large Reasoning Models (LRMs)! Since early this year, our team has released several RL+LLMs works (PRIME, TTRL, SimpleVLA, MARTI, SSRL, HPT), covering dense rewards, self-evolution, embodied AI, multi-agent, tool learning, and…
HOW INFORMATION FLOWS THROUGH TRANSFORMERS Because I've looked at those "transformers explained" pages and they really suck at explaining. There are two distinct information highways in the transformer architecture: - The residual stream (black arrows): Flows vertically through…
HOW INFORMATION FLOWS THROUGH TRANSFORMERS Because I've looked at those "transformers explained" pages and they really suck at explaining. There are two distinct information highways in the transformer architecture: - The residual stream (black arrows): Flows vertically through… https://t.co/IVsNUq4hUT
Super excited to share our work on variable-length discrete diffusion models! We design a diffusion process that iteratively unmasks tokens and simultaneously adds new tokens. This enables variable-length discrete planning as well as flexible text generation and reasoning!
Super excited to share our work on variable-length discrete diffusion models! We design a diffusion process that iteratively unmasks tokens and simultaneously adds new tokens. This enables variable-length discrete planning as well as flexible text generation and reasoning!
Ever wonder what's really happening when we use RL to teach LLMs to reason? 🤔 The process is full of mysteries. 🤯 What causes those sudden "aha moments" in training? 📏 Why does better reasoning often lead to longer answers ("length-scaling")? 📉 Why does token entropy often…
Ever wonder what's really happening when we use RL to teach LLMs to reason? 🤔 The process is full of mysteries. 🤯 What causes those sudden "aha moments" in training? 📏 Why does better reasoning often lead to longer answers ("length-scaling")? 📉 Why does token entropy often…
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