https://datafranca.org/wiki/index.php?action=history&feed=atom&title=UI-S1 2026-04-09T18:07:38Z Historique des versions pour cette page sur le wiki MediaWiki 1.39.5 https://datafranca.org/wiki/index.php?title=UI-S1&diff=117038&oldid=prev 2025-09-20T14:12:58Z

<p></p> <table style="background-color: #fff; color: #202122;" data-mw="interface"> <col class="diff-marker" /> <col class="diff-content" /> <col class="diff-marker" /> <col class="diff-content" /> <tr class="diff-title" lang="fr"> <td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">← Version précédente</td> <td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Version du 20 septembre 2025 à 10:12</td> </tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l12">Ligne 12 :</td> <td colspan="2" class="diff-lineno">Ligne 12 :</td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>  Advancing GUI Automation via Semi-online Reinforcement Learning</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>  Advancing GUI Automation via Semi-online Reinforcement Learning</div></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>  a novel approach for training GUI automation agents that bridges the gap between offline and online reinforcement learning.</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>  a novel approach for training GUI automation agents that bridges the gap between offline and online reinforcement learning.</div></td></tr> <tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>Semi-online Reinforcement Learning addresses the limitations of offline and online RL by simulating online RL on offline trajectories, achieving state-of-the-art performance in dynamic benchmarks.</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;"> </ins>Semi-online Reinforcement Learning addresses the limitations of offline and online RL by simulating online RL on offline trajectories, achieving state-of-the-art performance in dynamic benchmarks.</div></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>== Source ==</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>== Source ==</div></td></tr> </table>

Pitpitt https://datafranca.org/wiki/index.php?title=UI-S1&diff=117037&oldid=prev 2025-09-20T14:11:26Z

<p>Page créée avec « ==en construction== == Définition == XXXXXXXXX == Français == &#039;&#039;&#039; UI-S1&#039;&#039;&#039; == Anglais == &#039;&#039;&#039;UI-S1&#039;&#039;&#039; Advancing GUI Automation via Semi-online Reinforcement Learning a novel approach for training GUI automation agents that bridges the gap between offline and online reinforcement learning. Semi-online Reinforcement Learning addresses the limitations of offline and online RL by simulating online RL on offline trajectories, achieving state-of-the-art performa... »</p> <p><b>Nouvelle page</b></p><div>==en construction==<br /> <br /> == Définition ==<br /> XXXXXXXXX<br /> <br /> == Français ==<br /> &#039;&#039;&#039; UI-S1&#039;&#039;&#039;<br /> <br /> == Anglais ==<br /> &#039;&#039;&#039;UI-S1&#039;&#039;&#039;<br /> <br /> Advancing GUI Automation via Semi-online Reinforcement Learning<br /> a novel approach for training GUI automation agents that bridges the gap between offline and online reinforcement learning.<br /> Semi-online Reinforcement Learning addresses the limitations of offline and online RL by simulating online RL on offline trajectories, achieving state-of-the-art performance in dynamic benchmarks.<br /> <br /> == Source ==<br /> <br /> [https://huggingface.co/papers/2509.11543? Source : huggingface]<br /> <br /> [[Catégorie:vocabulary]]</div>

Pitpitt