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Spatial intelligence tasks are solved 5.6x faster with zero accuracy loss by reframing them as classification problems, eliminating autoregressive decoding entirely.
Tree search on on-policy trajectories dramatically boosts policy gradient learning by uncovering rare high-return paths, outperforming PPO on Atari and MuJoCo with minimal bias increase.
ABE-Ralph uncovers that LLMs often produce methodological hallucinations, leading to flawed scientific conclusions in AI research.
Recursive self-improvement in Macaron-V1 leads to continual learning that adapts to real-world experiences, setting a new standard for open agent models.
LongStraw enables RL post-training with over 2 million tokens on a fixed GPU budget, pushing the boundaries of context length in AI applications.