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“Memory is the mother of all wisdom.” — Aeschylus (525–426 BC). In humans, memory underpins learning, identity, and reasoning. In LLM agents, it is mostly missing. This series is about how modern agents can remember, guided by how brains do. 
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Model merging has recently emerged as a sophisticated method of “synaptic synthesis,” integrating specialized weights from disparate models into a singular, cohesive architecture. Just as an alchemist mixes chemicals in the hope of forging new materials that possess the superior properties of their originals, mixing neural networks allows us to synthesize specialized knowledge without the high cost of model retraining. It is especially relevant for Large Language Models (LLMs) as the cost of finetuning is huge. Moreover, as there are numerous pretrained models on different domains and modalities, it would be ideal if we could combine the models to create a universal master model that specializes in any topic. Read more
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Large language models are getting better at reasoning, not because we made them bigger, but because we finally learned how to teach them after pre-training,a.k.a., post-training. Continuing our series on RL for LLM reasoning, today’s blog reviews recent papers that boost LLM reasoning capability via post-training with RL. If you care about strengthening a model’s intrinsic reasoning capabilities rather than bolting on expensive test-time scaling or multi-sample decoding, this overview highlights the methods that genuinely transform the model. Read more
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In our last post, we warmed up with why reinforcement learning (RL) is a powerful paradigm for building smarter AI reasoners. Today, we zoom in on an exciting approach: using RL at inference time to improve large language model (LLM) reasoning on the spot. In particular, we explore ways to inject real-time reasoning into static LLMs. Let’s break down how RL can transform a frozen LLM into a more dynamic, reasoned thinker at runtime. Read more
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Large Language Models (LLMs) have shown remarkable capabilities across a range of natural language tasks. Yet, when you give them a problem that needs a bit of careful thinking, like a tricky math question or understanding a complicated document, suddenly they can stumble. It’s like they can talk the talk, but when it comes to really putting things together step-by-step, they can get lost. Read more