Opinion: API distillation is only a stepping stone for RL; autonomous iteration of GLM 5.2 can completely eliminate dependence on American models

ME AI According to observations from Beating monitoring, Google TPU software engineer Patrick Toulme said that there is a misunderstanding outside the industry about the claim that GLM 5.2 closes the gap with Opus by relying on distillation. The training challenge for large models on agent coding tasks lies in the “zero-gradient dilemma,” meaning that if the model cannot generate the correct execution path in the early stage, reinforcement learning cannot obtain gradient signals to trigger parameter updates. The purpose of distilling Claude or GPT-5.5 is only to provide seed answers during the cold-start phase, helping to bypass the zero-gradient dilemma.

Once the model passes the cold-start threshold, subsequent performance gains will no longer depend on distillation, but instead rely entirely on reinforcement learning’s hill-climbing algorithms for self-evolution. Toulme emphasized that GLM 5.2 already has the ability to independently generate successful paths, and it can iterate autonomously through reinforcement learning to reach higher levels, completely freeing itself from reliance on U.S. large models.

Redis founder Salvatore Sanfilippo added another possibility: although introducing a reasoning mode (distillation) via high-capability models is very useful for obtaining better RL signals, DeepSeek R0 practice has already shown that even in a pure cold start with no distillation seeding, reinforcement learning can still run on its own and achieve breakthroughs. He also believes that if it still needs to cross the cold-start threshold, large-model development can initially fine-tune using domestic open-source models such as DeepSeek-v3.2, rather than necessarily relying on U.S. APIs. (Source: BlockBeats)