Spark Strategic Policy-Aware Exploration via Dynamic Branching for Long-Horizon Agentic Learning
MACHINE LEARNING
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Advancing Open-source World Models
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Advancing Open-source World Models pic.twitter.com/jDjv2k0Tft
— AK (@_akhaliq) 29 janvier 2026Advancing Open-source World Models
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Career Path for Junior Developers in AI Era
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so much AI discourse – but what should junior developers be doing right now to improve their career prospects?
— DataRobot (@DataRobot) 29 janvier 2026
one take from a DR developer: pic.twitter.com/AKesfM7Khiso much AI discourse – but what should junior developers be doing right now to improve their career prospects? one take from a DR developer:
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Self-Distillation Fine-Tuning: On-Policy Continual Learning from Expert Demonstrations
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2026 is the year of continual learning And we are getting some amazing papers towards that This paper introduces Self-Distillation Fine-Tuning (SDFT): on-policy continual learning from expert demonstrations, with no explicit reward inference or engineering The trick here is:
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World Models Replacing Video Generation in 2026
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World models are going to take over in 2026. While video generation models are impressive dreamers, they aren't world simulators. Generative video models can produce stunning clips, but they hallucinate pixel transitions based on statistical correlations. This leads to
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Investigation: From Confident Gibberish to Scheduler Fix
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5/5 Wrote up the complete investigation-from confident gibberish to the scheduler fix. Includes the false starts (suspected CUDA bugs), the breakthrough (request tracking), and lessons for anyone running stateful models at scale. Read the blog:
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GPU Memory Utilization Bug: Token Classification Mismatch
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2/5 Used gpu_memory_utilization=0.2 to reproduce quickly, but this happens naturally when the scheduler runs out of token budget and GPU blocks get recycled. New request gets 1 token → misclassified as "decode" But num_computed_tokens=0 → should be "prefill".
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SSM Recurrence Security: Mamba State Propagation vs Attention Safety
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3/5 SSMs are recurrent, each token depends on previous state. Mamba decode reads state → updates it. Misclassified request reads garbage → propagates recursively through all tokens. Attention writes K/V first, then attends. Safe even if misclassified.
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Debugging vLLM: Silent Corruption Bug in Jamba RL Training
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1/5 Debugging vLLM: The silent corruption bug
1/1000 Jamba generations collapsed into confident gibberish during RL training. No crashes, no errors, just wrong outputs with high logprobs. The culprit? A scheduler edge case that only triggers under memory pressure. -
Reasoning Models: The Next Leap In AI With K2-Think
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Why Reasoning Models Are The Next Leap In AI: Inside MBZUAI's K2-Think https://t.co/8u9SvKBQju
— Bernard Marr (@BernardMarr) 29 janvier 2026Why Reasoning Models Are The Next Leap In AI: Inside MBZUAI's K2-Think