arXiv:2611.05751v1 Announce Type: cross Abstract: Large language models (LLM) have been the key focus of recent machine learning breakthroughs. As the model size grows exponentially, the computation needed for training is astronomical. A GPU can do a huge amount of arithmetic. Ho…
arXiv:2611.05841v1 Announce Type: new Abstract: This vision paper argues that distributed learning, including federated and decentralized, should become a first-class paradigm in LLM post-training, not merely a privacy add-on. Current methods like RLHF and DPO rest on an invisibl…
arXiv:2611.05556v1 Announce Type: new Abstract: LLM-as-a-judge is a common substitute for costly human evaluation, and aggregating several judges is the standard remedy for any single judge's bias. Judge pools mix judges of very different reliability, while the human-labeled sets…
arXiv:2603.03415v4 Announce Type: replace-cross Abstract: In this work, we investigate how the internal representations of Large Language Models (LLMs) change with inputs of increasing difficulty. Although sparsity changes may not be obvious for individual samples, dataset-level …
arXiv:2602.06600v3 Announce Type: replace Abstract: Recent work improves reasoning in large reasoning models (LRMs) by test-time scaling through self-consistency and parallel thinking, adding generic "thinking tokens", or presenting the question twice, as in re-reading (RE2). How…
arXiv:2611.00504v1 Announce Type: cross Abstract: LLMs are increasingly used to design heuristics for computer systems. However, most synthesis frameworks evaluate candidates by their average performance, which can mask severe regressions on specific workloads. Even adding advers…
arXiv cs.CL
TIER_1English(EN)·Arda Uzunoglu, Mahler Revsine, Michael Schatz, Benjamin Van Durme, Mehrdad Farajtabar, Daniel Khashabi, Eric Nalisnick·
arXiv:2611.03617v1 Announce Type: new Abstract: Large language models (LLMs) show growing promise for scientific discovery, yet basic questions remain about what enables reliable discovery. How can we trace the scientific discovery capacity of LLMs to their training data? We stud…
arXiv cs.CL
TIER_1English(EN)·Ahmad Jarjis Hasan, Jonathan Lwowski·
arXiv:2611.03377v1 Announce Type: cross Abstract: Every multimodal-LLM multiple-choice benchmark ends with a scoring rule that converts a model's response, often free-form text rather than a clean option letter, into a right/wrong verdict, buried in its code. Unlike the dataset, …
arXiv:2610.41149v1 Announce Type: new Abstract: Agentic systems increasingly gate individual steps with a pointwise judge that scores a response against a criterion. Small language models are attractive for this role because they can be served and specialized efficiently. Such a …
arXiv:2505.14756v3 Announce Type: replace Abstract: Bayesian optimization (BO) is a sequential decision-making tool widely used for optimizing expensive black-box functions. Recently, Large Language Models (LLMs) have shown remarkable adaptability in low-data regimes, making them…
arXiv:2610.41486v1 Announce Type: new Abstract: While prior work has highlighted that systematic nation-level bias in large language models (LLMs) can pose operational risks for international relations (IR) applications, many existing evaluations still lack a clearly specified un…
arXiv:2506.20856v1 Announce Type: cross Abstract: Memorization in large language models (LLMs) makes them vulnerable to data extraction attacks. While pre-training memorization has been extensively studied, fewer works have explored its impact in fine-tuning, particularly for LoR…
arXiv cs.AI
TIER_1English(EN)·Zhiliang Chen, Alfred Wei Lun Leong, Shao Yong Ong, Apivich Hemachandra, Gregory Kang Ruey Lau, Chuan-Sheng Foo, Zhengyuan Liu, Nancy F. Chen, Bryan Kian Hsiang Low·
arXiv:2602.08351v3 Announce Type: replace-cross Abstract: Fine-tuning an LLM to maximize performance on a downstream task requires finding the optimal data and model configuration. This is a black-box optimization problem that Bayesian optimization (BO) addresses by sequentially …
arXiv cs.AI
TIER_1English(EN)·Mohammadsina Almasi, Andrea Carbonati, Hadis Anahideh·
arXiv:2603.28959v2 Announce Type: replace-cross Abstract: The exploration-exploitation trade-off is central to sequential decision-making and black-box optimization, yet how Large Language Models (LLMs) reason about and manage this trade-off remains poorly understood. Unlike Baye…
Lobsters — AI tag
TIER_1English(EN)·rikiyat.github.io via timschumi·
<h4>Choose among SFT, preference optimization, and reinforcement learning — and measure whether they improve real tasks.</h4><p>Post-training changes model behavior using demonstrations, preferences, or rewards after base pre-training. The engineering problem is choosing a feedba…
Medium — fine-tuning tag
TIER_1Deutsch(DE)·Vedanth·
<blockquote> <p><strong>TL;DR:</strong> Picking the wrong base model can sink your fine-tuning project. Compare Llama, Mistral, Qwen and more on context, licence, cost and hardware.</p> </blockquote> <p>Fine‑tuning large language models (LLMs) has become the go‑to strategy for ad…
<!-- SC_OFF --><div class="md"><p>Link: <a href="https://rikiyat.github.io/blog/weight-space-programming/">https://rikiyat.github.io/blog/weight-space-programming/</a></p> <p>Snippet: “We find that frontier LLMs are surprisingly good at <strong>natively reading and writing neural…
LLMs as numerical optimizers: close answers, broken rules LLMs produce near-optimal solutions to optimization problems but persistent numerical errors limit their use as standalone solvers. https://www. notatechguy.com/llms-as-numeri cal-optimizers-close-answers-broken-rules/ # N…
