As a result, 12% of the papers in the review pool were desk rejected. Pulling this off in a timely manner was not easy, and was only possible thanks to our wonderful and dedicated reviewers and ACs!
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𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗱𝗲𝗮𝗱𝗹𝗶𝗻𝗲: 𝟭 𝗝𝘂𝗻𝗲 𝟮𝟬𝟮𝟲
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#AthenaRC#LanguageTechnology#Education
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Here are some that I am just copy pasting from my chat (just SFT and preference tuning, no RLVR/GRPO here):
Self-Rewarding Language Models
UltraFeedback
Few-Shot Preference Optimization
Skywork-Reward
OpenRLHF
JudgeLM
Alignment-Weighted DPO
Where does output diversity collapse in post-training?
Category-Adaptive Safety Alignment
SFT-GO
Online DPO & Iterative Training
f-PO: Generalized Preference Optimization
GOPO: Policy Optimization using Ranked Rewards
One Adapts to Any: Meta Reward Modeling for Personalized LLM Alignment
Some technical reports like the old Llama 3 and Deepseek was there too
Thrilled to see such a fantastic breakdown of our latest paper on output diversity collapse in post-training (with @Xingwei__Tan, @nikaletras, @SheffieldNLP)! 🚀
@neural_avb did a phenomenal job summarizing our work. Check it out below! 👇
I have been studying a lot of post-training paper to prepare for my next video.
This morning I am studying a banger from this week: "Where does output diversity collapse in post-training?"
One of the more educational papers I have read recently.
Basically after post-training LLMs often produce less varied answers than their base versions. This paper runs experiments to figure out where exactly this diversity is lost, the effects of distillation vs instruction-tuning vs RLVR, etc.
Their big hypothesis is that the diversity cannot be recovered at inference through sampling techniques or CoT. The collapse irreversibly happens during training.
@_Suresh2@neural_avb If you use narrow, two-teacher distillation (Think), almost all the diversity collapses right at the SFT stage. But if you use broader, multi-source data for SFT (Instruct), the model retains more variety initially, which actually shifts the sharpest diversity drop to the DPO.
This work is my favourite one in many ways.
If you are interested in cross-lingual transfer/adaptation, please check this out! Also, if you plan to attend ACL in July, please stop by our poster and let’s have a chat!
When we adapt LLMs to other languages using unlabeled target language data, they tend to lose their core instruction-following and reasoning abilities.
🚨 New #ACL2026 paper w/ @_gucciiiii, T. Morishita & @AlineVillav on how to mitigate this catastrophic forgetting 👇
Applications for our fully-funded 3.5 year #AI#PhD studentship on “#Metamodelling of #Speech Domains” starting in Sept closes TODAY!
Hurry! Apply now!
More info and to apply: https://t.co/PoNwTtx756