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https://arxiv.org/pdf/2511.10395

What if AI agents could teach themselves? In this episode, we dive into AgentEvolver, a groundbreaking framework from Alibaba's Tongyi Lab that flips the script on how we train autonomous AI agents.

Traditional agent training is brutal: you need manually crafted datasets, expensive random exploration, and mountains of compute. AgentEvolver introduces a self-evolving system with three elegant mechanisms that let the LLM drive its own learning:

Self-Questioning – The agent explores environments and generates its own tasks through curiosity-driven interaction, eliminating the need for hand-crafted training data.

Self-Navigating – Instead of random exploration, the agent builds an experience pool, retrieves relevant past solutions, and uses hybrid rollouts that mix experience-guided and vanilla trajectories. They tackle the off-policy learning problem with selective boosting for high-performing trajectories.

Self-Attributing – Fine-grained credit assignment that goes beyond simple trajectory-level rewards, using step-level attribution to figure out which specific actions and states actually contributed to success.

We break down the advantage calculation mechanics, discuss how they handle the inference/learning sample mismatch through experience stripping, and explore why broadcasting trajectory advantages to token-level might be leaving performance on the table.

The results are compelling: their 7B model outperforms much larger baselines on AppWorld and BFCL-v3 benchmarks while reducing training steps by up to 67%. This isn't just another incremental improvement – it's a fundamental shift from human-engineered training pipelines to LLM-guided self-improvement.

Key topics: reinforcement learning for LLMs, experience replay, credit assignment, autonomous task generation, agent systems, GRPO/PPO optimization