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Public story · 2026-03-19 · source-backed
ArXiv 2603.17310 introduces training rewards based on AUC of information gain across reasoning steps rather than final-answer correctness. Directly targets "reasoning theater" where extended chain-of-thought adds tokens without proportional accuracy gains. Compatible with existing RLHF pipelines. arXiv
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Researchers reveal that Direct Preference Optimization implicitly operates over a full preference graph, meaning it extracts more signal from existing datasets than anyone realized. Practical implication: your existing RLHF data may be more valuable than you think.
ArXiv 2603.17368 proposes evaluating safety policy before chain-of-thought generation rather than after. Models that reason first and apply safety second can be manipulated through the reasoning trace itself. Reordering substantially improves alignment without degrading benchm...
This paper models LLM training as information transmission over a noisy channel via the Shannon-Hartley theorem. Existing power-law scaling laws can't explain catastrophic overtraining or quantization-induced degradation. This framework predicts when scaling breaks down. Direc...
ArXiv paper 2603.14805 argues the primary bottleneck in scaling agentic development is knowledge architecture — "skills" (composable, governance-aware units encoding institutional knowledge) are the right primitive for agents, not raw docs or in-context retrieval. Directly rel...
The 2026 consensus: exhaust prompting and retrieval before touching weights, and when you fine-tune, use a thin LoRA/QLoRA adapter paired with retrieval, not full fine-tuning. DPO is the default over RLHF when you have preference pairs. Decision rule: RAG for knowledge that ch...
Reveals the "iterative refinement paradox": as models iteratively improve code for functional correctness, security properties silently degrade. Introduces counterexample-guided synthesis to maintain security invariants. Directly relevant to anyone using AI code iteration work...
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