World models trained via latent dynamics offer a scalable path for embodied planning, yet existing methods suffer from representational collapse, computational overhead, and limited long-horizon capability. We introduce MemWM, a memory-enhanced latent world model that augments JEPA-style prediction with a content-addressable memory bank. MemWM learns high-level representations directly from observations and uses them to condition predictions via adaptive layer normalization, complementing low-level action-conditioned dynamics within a shared latent space. To prevent memory slots from collapsing into identical representations, we propose two complementary losses that encourage structural diversity across slots and balanced utilization across time steps. On PushT, TwoRoom, Reacher, and OGBCube, MemWM attains an average success rate of 89.9%, matching DINO-WM (89.3%) while using 4$\times$ fewer trainable parameters and no pretrained encoder, and improving over LeWM by 4.1%.
We present T^\star, a simple TraceRL-based curriculum for progressive block-size scaling in masked diffusion language models (MDMs).Starting from an AR-initialized small-block MDM, T^\star gradually increases the block size while re-optimizing the denoising policy at each stage, enabling higher-parallelism decoding with limited degradation on math reasoning benchmarks. Across two SDAR scales and three benchmarks, T^\star consistently outperforms direct large-block TraceRL and is substantially more stable during training. Our schedule analysis suggests that the learned policy does not simply revert to a strictly left-to-right order; instead, it retains block-size-specific non-monotone updates while improving accuracy.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) · 2026 · 正式发表