…We further identify that the conventional MTP training objectives ( cross-entropy or KL) are suboptimal in such settings, and therefore we propose a novel end-to-end TV loss that directly optimizes…
…With a straight-through estimator and an auxiliary regularization loss , BEAM induces dynamic expert sparsity through end-to-end training while maintaining model capability. We further implement an efficient custom CUDA kernel…
…Generated by Qwen/Qwen2.5-Coder-32B-Instruct Deep Research (DR) has emerged as a new agentic paradigm to tackle complex, open-ended research tasks, demanding systems that can iteratively frame problems…
…Further analysis uncovers sharp capability boundaries across task domains, divergent behavioral strategies among models, and a disconnect between token consumption and output quality , providing fine-grained diagnostic signals beyond what aggregate metrics…
…Furthermore, improvements from model scaling quickly saturate, strong general reasoning does not reliably translate to creative affordance discovery, and common inference-time strategies such as Chain-of-Thought yield limited gains. These…
…Darrien McKenzie , , Abstract Reinforcement learning approaches for improving LLM reasoning capabilities are enhanced by a Bayesian Manifold Curriculum framework that structures problem sampling based on task manifold relationships and endogenous non-stationarity…
…To this end, we propose DOPD, an advantage-aware dual distillation paradigm that dynamically routes token-level supervision between privileged teacher and privileged student policies based on their advantage gap and relative…
…Unifying Retrieval and Listwise Reranking via End-to-End Joint Training with Residual Passage Compression (2026) Please give a thumbs up to this comment if you found it helpful! If you want…
…The suite includes Paper-Search, a standalone multi-source literature search skill; Scoop-Check, a standalone prior-art collision checker for novelty claims; and IdeaSpark, the end-to-end skill that composes…
…Josef Chen Abstract Multi-model systems face fundamental accuracy limits determined by the rate at which all models fail simultaneously, regardless of their individual correlations or ensemble strategies. Generated by Qwen/Qwen2…