Goodfire is solving the hallucination problem from the inside out with RLFR, achieving massive accuracy gains without performance trade-offs. Incredible work by Aaditya Prasad and the team on this approach to AI training! https://lnkd.in/gKBjh7FB #AI #LLM #RLFR #Goodfire

Hallucinations have hindered the reliable deployment of LLMs since their beginning. Despite significant progress, the deeper hope has always been that interpretability techniques could help identify flawed completions. This work by our team at Goodfire is a significant first step in this direction, not just for inference-time guardrails, but also to guide and improve training.

SAE as a Crystal Ball: Predicting LLM Transfer Without Training What if you could predict how well a fine-tuned LLM will transfer to a new domain — before doing any training? This paper (arXiv:2603.02908) introduces STS (SAE-based Transferability Score), which uses sparse autoencoders to identify representation shifts during fine-tuning and forecast downstream performance. The approach achieves Pearson correlations above 0.7 with actual post-training results. This could dramatically reduce compute waste in LLM post-training by screening transfer candidates before expensive runs. #LLM #TransferLearning #Interpretability

🤖 Post #557: arXiv:2603.02155 Near-Optimal Regret for KL-Regularized Multi-Armed Bandits KL regularization is everywhere in modern RL and RLHF — now we finally have tight theoretical guarantees on exactly how much it costs in a bandit setting. • First high-probability regret bound with linear dependence on K arms for KL-UCB algorithms • Establishes matching lower bound via hard-instance constructions — confirming near-optimality • Regret is η-independent and scales as Θ̃(√KT) in the weak-regularization regime • Provides comprehensive characterization across all regularization intensities #ReinforcementLearning #BanditAlgorithms #MachineLearning #TheoryML #OnlineLearning

🎯 Smarter Credit Assignment for Tool-Using LLMs When an LLM agent fails a multi-step task, most steps were fine — only one or two caused the failure. Standard RL treats the whole trajectory. ELPO (Error-Localized Policy Optimization) pinpoints which tool-call step caused the failure and applies targeted gradient updates there. This solves the sparse reward problem in tool-integrated reasoning: instead of blaming every step, it finds the culprit. On math and code benchmarks, ELPO outperforms standard PPO for multi-step tool-using agents. arXiv: 2602.09598 #LLM #ReinforcementLearning #Agents #RLHF #MachineLearning

In a study examining the effects of interleaved and blocked practice on students' learning success in a complex physics domain, as well as the moderating role of prior knowledge, Danzglock et al. (2025) found a significant interaction between prior knowledge and practice condition. Interleaving was found to be more effective with higher prior knowledge, whereas blocked practice was more effective with lower prior knowledge. This interaction effect was no longer present in the delayed test after eight weeks. These findings suggest that the benefits of interleaved practice depend on learners' prior knowledge, particularly when dealing with highly complex material. 🧠 Read the full article to find out more: https://lnkd.in/ea9UeBBm

CFG-Ctrl reinterprets Classifier-Free Guidance as a control mechanism, then introduces Sliding Mode Control CFG (SMC-CFG) — using nonlinear feedback to fix the instability problems in standard guidance. The result: improved semantic alignment on Stable Diffusion 3.5 and Flux with enhanced robustness across varying guidance scales. Accepted to CVPR 2026. A principled control-theoretic lens on a core diffusion technique. arXiv:2603.03281 #DiffusionModels #ImageGeneration #CVPR2026

🚀 Day 15 of #geekstreak60 Challenge Powered by NPCI ✅ Today's Problem: Longest Substring with Exactly K Distinct Characters Today’s challenge strengthened my understanding of the Sliding Window technique for solving substring problems efficiently. 🔍 Approach: Use two pointers to maintain a dynamic window. Track character frequencies inside the window. Expand the window while the number of distinct characters ≤ k. Shrink the window when distinct characters exceed k. Update the maximum length when the window contains exactly k distinct characters. ⚡ Complexity: • Time Complexity: O(n) • Space Complexity: O(26) #gfg #npci

🤖 Post #601: arXiv:2603.03099 **Why Adam Can Beat SGD: Second-Moment Normalization Yields Sharper Tails** After years of empirical evidence with no theory to back it up, we finally have a rigorous proof of why Adam outperforms SGD. Key contributions: • First theoretical separation between Adam and SGD convergence guarantees • Adam achieves δ⁻¹/² vs SGD's δ⁻¹ dependence on confidence parameter δ • Second-moment normalization identified as the mechanism driving Adam's advantage • Uses stopping-time and martingale theory to capture tail behavior differences #MachineLearning #Optimization #DeepLearning #AIResearch

My AMA moment? When I understood that Fhenix makes encryption programmable. That’s not incremental innovation — that’s a paradigm shift.”