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On August 14, 2026, Ant's ASystem team launched the AReno local large-model post-training toolkit. On the same day, Ant Ling released a practice article describing how AReno was used on DGX Spark to perform Agentic RL on Ling-3.0-tiny, using tic-tac-toe as the minimal validation task and completing a four-step closed loop of baseline evaluation, reward definition, post-training, and re-testing in the same environment. The experiment used GSPO to train for 400 steps; rollout/rewards_mean rose from about -0.5 to around 0.4, response_len dropped to about 850 tokens, and in the re-test, tool calls and move selection were more stable. Ling-3.0-tiny is a hybrid reasoning model with 7.9B total parameters and 1.3B activated parameters, and BF16, FP8, and INT4 versions have been open-sourced; AReno provides a Docker reproduction tutorial. The toolkit has now been made public together with the above practice.
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Ant Ling closes the single-machine Agentic RL loop with Ling-3.0-tiny and ARenoDevelopments
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Aug 14
Ant Ling: Developer BlogFirst-party
Ant Ling closes the single-machine Agentic RL loop with Ling-3.0-tiny and AReno
Ant Ling has published a practice article describing how it used the local post-training toolkit AReno on a DGX Spark to perform Agentic RL on Ling-3.0-tiny, using tic-tac-toe as a minimal validation task to complete the four-step loop of baseline evaluation, Reward definition, post-training, and re-evaluation in the same environment. The experiment used GSPO for 400 training steps; rollout/rewards_mean rose from about -0.5 to around 0.4, response_len dropped to about 850 tokens, and tool calling and move selection became more stable in the re-evaluation. Ling-3.0-tiny is a hybrid reasoning model with 7.9B total parameters and 1.3B activated parameters; BF16, FP8, and INT4 versions have been open-sourced, and AReno provides a Docker reproduction tutorial.