“What if AI systems could learn how to improve themselves not just at the level of weights or prompts, but at the level of strategy itself? In this post, we show how to build such a system, powered by symbolic rules and reflection.
The paper Symbolic Agents: Symbolic Learning Enables Self-Evolving Agents introduces a framework where symbolic rules guide, evaluate, and evolve agent behavior.
Chain-of-thought is powerful, but which chain? Short explanations work for easy tasks, long reflections help on hard ones, and code sometimes beats them both. What if your model could adaptively pick the best strategy, per task, and improve as it learns?
The Adaptive Reasoning Model (ARM) is a framework for teaching language models how to choose the right reasoning format direct answers, chain-of-thoughts, or code depending on the task. It works by evaluating responses, scoring them based on rarity, conciseness, and difficulty alignment, and then updating model behavior over time.
The General Reasoner paper shows how we can train LLMs to reason across domains using diverse data and a generative verifier. In this post, I walk through our open-source implementation showing how we built a modular reasoning agent capable of generating multiple hypotheses, evaluating them with an LLM-based judge, and selecting the best answer.
We built a GeneralReasonerAgent that:
cot, debate, verify_then_answer, etc.)All of this was integrated with our existing co_ai framework, which includes: