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Our benchmark and RL recipe help smaller models hold their ground.</p></div></div></div><div class="_linkIcon_yt1oq_29" aria-hidden="true"><svg xmlns="http://www.w3.org/2000/svg" width="22" height="22" viewbox="0 0 22 22" fill="none"><path d="M1 1H21M21 1V21M21 1L1 21" stroke="currentColor" stroke-width="1.5"></path></svg></div></a><a href="https://arxiv.org/abs/2509.13399" class="_linkFeedItem_yt1oq_10"><div class="_linkFeedContent_yt1oq_47"><div class="_linkFeedHeader_yt1oq_54"><span class="content-label _eyebrow_yt1oq_62">ICLR 2026</span><h3 class="h5 _linkFeedTitle_yt1oq_38">EdiVal-Agent</h3></div><div class="_linkFeedBody_yt1oq_72"><div class=""><p>Object-level grading of multi-turn image editing, exposing where today's best editors silently break.</p></div></div></div><div class="_linkIcon_yt1oq_29" aria-hidden="true"><svg xmlns="http://www.w3.org/2000/svg" width="22" height="22" viewbox="0 0 22 22" fill="none"><path d="M1 1H21M21 1V21M21 1L1 21" stroke="currentColor" stroke-width="1.5"></path></svg></div></a><a href="https://arxiv.org/abs/2509.26495" class="_linkFeedItem_yt1oq_10"><div class="_linkFeedContent_yt1oq_47"><div class="_linkFeedHeader_yt1oq_54"><span class="content-label _eyebrow_yt1oq_62">ICLR 2026</span><h3 class="h5 _linkFeedTitle_yt1oq_38">OffTopicEval</h3></div><div class="_linkFeedBody_yt1oq_72"><div class=""><p>Give an LLM a job and clear boundaries. It still answers off-topic questions, almost every time.</p></div></div></div><div class="_linkIcon_yt1oq_29" aria-hidden="true"><svg xmlns="http://www.w3.org/2000/svg" width="22" height="22" viewbox="0 0 22 22" fill="none"><path d="M1 1H21M21 1V21M21 1L1 21" stroke="currentColor" stroke-width="1.5"></path></svg></div></a><a href="https://arxiv.org/abs/2506.03337" class="_linkFeedItem_yt1oq_10"><div class="_linkFeedContent_yt1oq_47"><div class="_linkFeedHeader_yt1oq_54"><span class="content-label _eyebrow_yt1oq_62">ICLR 2026</span><h3 class="h5 _linkFeedTitle_yt1oq_38">Zeroth-Order Federated LLM Fine-Tuning</h3></div><div class="_linkFeedBody_yt1oq_72"><div class=""><p>Sparse updates make syncing cheap enough to go frequent, neutralizing non-IID drift.</p></div></div></div><div class="_linkIcon_yt1oq_29" aria-hidden="true"><svg xmlns="http://www.w3.org/2000/svg" width="22" height="22" viewbox="0 0 22 22" fill="none"><path d="M1 1H21M21 1V21M21 1L1 21" stroke="currentColor" stroke-width="1.5"></path></svg></div></a><a href="https://openreview.net/forum?id=Xn6EnJZghu" class="_linkFeedItem_yt1oq_10"><div class="_linkFeedContent_yt1oq_47"><div class="_linkFeedHeader_yt1oq_54"><span class="content-label _eyebrow_yt1oq_62">ICLR 2026</span><h3 class="h5 _linkFeedTitle_yt1oq_38">LLM Unlearning Reframed as Retrieval</h3></div><div class="_linkFeedBody_yt1oq_72"><div class=""><p>Smarter data selection pushes the forget-vs-retain frontier past oracle sampling.</p></div></div></div><div class="_linkIcon_yt1oq_29" aria-hidden="true"><svg xmlns="http://www.w3.org/2000/svg" width="22" height="22" viewbox="0 0 22 22" fill="none"><path d="M1 1H21M21 1V21M21 1L1 21" stroke="currentColor" stroke-width="1.5"></path></svg></div></a><a href="https://arxiv.org/abs/2512.03759" class="_linkFeedItem_yt1oq_10"><div class="_linkFeedContent_yt1oq_47"><div class="_linkFeedHeader_yt1oq_54"><span class="content-label _eyebrow_yt1oq_62">ICLR 2026</span><h3 class="h5 _linkFeedTitle_yt1oq_38">Principled RL for Diffusion LLMs</h3></div><div class="_linkFeedBody_yt1oq_72"><div class=""><p>Token-level RL doesn't fit diffusion LLMs. Treat the whole sequence as one action, 20–40 point gains.</p></div></div></div><div class="_linkIcon_yt1oq_29" aria-hidden="true"><svg xmlns="http://www.w3.org/2000/svg" width="22" height="22" viewbox="0 0 22 22" fill="none"><path d="M1 1H21M21 1V21M21 1L1 21" stroke="currentColor" stroke-width="1.5"></path></svg></div></a><a href="https://arxiv.org/abs/2604.03911" class="_linkFeedItem_yt1oq_10"><div class="_linkFeedContent_yt1oq_47"><div class="_linkFeedHeader_yt1oq_54"><span class="content-label _eyebrow_yt1oq_62">ICLR 2026</span><h3 class="h5 _linkFeedTitle_yt1oq_38">Align Your Structures for Molecular Dynamics</h3></div><div class="_linkFeedBody_yt1oq_72"><div class=""><p>Pretrains on static molecular structures, stitches them into dynamics trajectories, bypassing simulation data scarcity.</p></div></div></div><div class="_linkIcon_yt1oq_29" aria-hidden="true"><svg xmlns="http://www.w3.org/2000/svg" width="22" height="22" viewbox="0 0 22 22" fill="none"><path d="M1 1H21M21 1V21M21 1L1 21" stroke="currentColor" stroke-width="1.5"></path></svg></div></a><a href="https://tangoflux.github.io/" class="_linkFeedItem_yt1oq_10"><div class="_linkFeedContent_yt1oq_47"><div class="_linkFeedHeader_yt1oq_54"><span class="content-label _eyebrow_yt1oq_62">ICLR 2026</span><h3 class="h5 _linkFeedTitle_yt1oq_38">TangoFlux</h3></div><div class="_linkFeedBody_yt1oq_72"><div class=""><p>515M params, 30s of studio audio in under 4 seconds. Aligned by ranking its own outputs.</p></div></div></div><div class="_linkIcon_yt1oq_29" aria-hidden="true"><svg xmlns="http://www.w3.org/2000/svg" width="22" height="22" viewbox="0 0 22 22" fill="none"><path d="M1 1H21M21 1V21M21 1L1 21" stroke="currentColor" stroke-width="1.5"></path></svg></div></a><a href="https://arxiv.org/abs/2510.02295" class="_linkFeedItem_yt1oq_10"><div class="_linkFeedContent_yt1oq_47"><div class="_linkFeedHeader_yt1oq_54"><span class="content-label _eyebrow_yt1oq_62">ICLR 2026</span><h3 class="h5 _linkFeedTitle_yt1oq_38">Video Native Sparse Attention</h3></div><div class="_linkFeedBody_yt1oq_72"><div class=""><p>Learnable sparse attention for video. 3.6% attention budget at 128K tokens, accuracy still improves.</p></div></div></div><div class="_linkIcon_yt1oq_29" aria-hidden="true"><svg xmlns="http://www.w3.org/2000/svg" width="22" height="22" viewbox="0 0 22 22" fill="none"><path d="M1 1H21M21 1V21M21 1L1 21" stroke="currentColor" stroke-width="1.5"></path></svg></div></a><a href="https://arxiv.org/abs/2510.02676" class="_linkFeedItem_yt1oq_10"><div class="_linkFeedContent_yt1oq_47"><div class="_linkFeedHeader_yt1oq_54"><span class="content-label _eyebrow_yt1oq_62">ICLR 2026</span><h3 class="h5 _linkFeedTitle_yt1oq_38">Exponent-Concentrated FP8</h3></div><div class="_linkFeedBody_yt1oq_72"><div class=""><p>Model weight exponents cluster into 2–3 bits of entropy. 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<p>&nbsp;</p>
<p><strong>Caia Costello, Simon Guo, Anna Goldie, and Azalia Mirhoseini — ICLR 2025 workshop</strong></p></div></div><a href="https://arxiv.org/abs/2504.18116" class="button button--tertiary accordionItemCta" aria-label="Read more (opens in a new tab)" target="_blank" rel="noopener noreferrer">Read more</a></div></div></div><div class="accordionItem"><div class="accordionItemNumberColumn"><span class="h5" aria-hidden="true">05</span></div><div class="accordionItemContentColumn"><div class="accordionItemHeaderWrapper accordionItemHeaderHorizontal"><h3 class="accordionItemHeader"><span class="accordionItemTitle">NeoBERT</span></h3></div><div class="accordionItemContent "><div class="accordionItemRich"><div><p>A next-generation BERT</p>
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<p><strong>Lola Le Breton, Quentin Fournier, Mariam El Mezouar, and Sarath Chandar — TMLR 2025</strong></p></div></div><a href="https://arxiv.org/pdf/2502.19587" class="button button--tertiary accordionItemCta" aria-label="Read more (opens in a new tab)" target="_blank" rel="noopener noreferrer">Read more</a></div></div></div></div></div><!--/$--></div><!--/$--></div></div></div></div><div class="sectionBorder"></div></section>
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