Antonio Montano ☼

China cracked recursive self-improvement 🤯 Tencent dropped 27B model that rewrites, retrains, and refines itself.. with zero human in the loop. It’s called "Terminal-RSI" and it solves one of the biggest problems with AI. They are incredibly lazy. Usually, an agent will make partial progress, claim it succeeded, and completely fail the final environment verifier check. this new pipeline forces agents to actually finish the job. here’s how it works: they built a recursive loop with two alternating phases.. - RSR (recursive self-rewrite): the model fine-tunes itself on its own successfully rewritten trajectories - RPR (recursive policy-refine): it then runs reinforcement learning (ppo) using a "critic" inherited directly from the previous stage to lock in those discovered behaviors - Loop: the training sequence leapfrogs itself: Base → RSR-1 → RPR-1 → RSR-2 → RPR-2 and the numbers are insane.. they applied this to a qwen3.8-27b base model. across the four "terminal-bench" suites, the agent's average success rate jumped from 20.68% all the way to 50.58%. on the absolute hardest benchmark suite (terminal-bench 4.0), the base model scored a flat 0.00%. after terminal-rsi? it hit 19.84% (putting it right on par with reference models like gemini 3.8 flash). the secret sauce? they fixed the reward system. instead of giving the ai "dense rewards" for just trying or passing semantic checks, they switched to a "success-prioritized reward" (spr). it mathematically separates strict success from partial credit. basically, the agent only gets the massive reward payout if the code actually passes the final terminal gate.. so it stops faking success. the gap between partial local repairs and strict terminal verification is closing fast. we are watching agents build their own training environments and level up autonomously.. the future is wild.