---
title: "ICLR2025 Saturday Posters"
source: https://www.jemoka.com/posts/kbhiclr2025_saturday_posters/
---

ICLR2025 Cassidy: AssistanceZero Train reward predictor to also have rewards at test time MCTS Learn to match root node KL ICLR2025 Liu: synthesizing programmatic reinforcement learning policies with LLM guided search Hill climbing with partial mutations of generated programs of LLMs
ICLR2025 Weller: l PromptTrirver ??
ICLR2025 Yu: robust LLM safeguard via refusal feature adversarial training With mechanistic interpretability, we can find a sub space which is correlated with refusal, pull that up
ICLR2025 Thrush: improving pre-training data using complexity correlation For each pre-training data domain, measure perplexity using existing language model, wrong correlations, train a fast text data sampler using samples of high correlations with ppl
ICLR2025 Muenninghoff: generative representational instruction tuning Train your model to do both the instructions and embeddings
ICLR2025 Aycock: can LM really learn to translate a low resource language from grammar books LLM&rsquo;s mostly learn parallel examples from grammar book in order for translation, gain from actually having grammar comes only in linguistic tasks
ICLR2025 Kaplan: from tokens to words Interpretability results show that last token embeddings is often how multiword tokens are represented in language model; so, why don&rsquo;t we use the last token embedding as new embedding to insert more vocab into the language model, also, for typos, we can use logit lens to trace at one point of a typo becomes corrected
ICLR2025 Kumarappan: lifeline learning for formal theorem proving lean prover; tree research with intermediate rewards to identify useful partial steps, online learning using these steps, sequence problems in curriculum learning fashion to build on using simple proofs
ICLR2025 Goyal: context parametric conversion Although instruction tuning improves context reliance initially, context reliance drops after time of in instruction fine-tuning despite standard performance benchmarks increasing
ICLR2025 Wan: self improving many shot reasoners for in context learning, run Bayesian optimization steps on some of the outputs, and use that to generate new in context examples
ICLR2025 Hsu: grounding by trying Do you RAG by trying some queries and for the good ones do SFT and then DPO
ICLR2025 Jia: your proofs are use for optimization based jailbreaking update multiple tokens at once in order to make attack efficiency faster
ICLR2025 Kang: visual attention sink in large language models Visual language models tend to attend to stuff that is kind of irrelevant, where a good intervention is to distribute attention weights from irrelevant things to relevant things
ICLR2025 Huang: steering LLM behavior with concept activation factors Method: extract last token embedding using positive and negative instructions. From there, steer LLMs using these in bedding
ICLR2025 Wang: perplexity trap documents, under semantic permutation, is considered more relevant when the perplexity is lower
