7.9B total. 1.3B active.
One DGX Spark. No cluster.
Ling-3.0-tiny × ASystem AReno closes the Agentic RL post-training loop—locally, on a single DGX Spark.
Running locally is only step one. Ling-3.0-tiny makes iteration practical; ASystem AReno turns task feedback into a trainable, retestable loop on one node.
Tic-tac-toe is the minimal task for validating an end-to-end Agentic RL loop.
Ling-3.0-tiny is post-trained with ASystem AReno on one DGX Spark.
Same model. Same environment. Before vs. after post-training.
Result: more stable tool calls and better move selection.
On a single DGX Spark, the ASystem AReno team post-trained Ling-3.0-tiny with GSPO for 400 steps.
• rollout/rewards_mean: ~-0.5 → ~0.4
• response_len: ↓ to ~850 tokens
One curve tracks stronger task feedback. The other shows shorter, more focused responses.
Together, they explain the shift seen in the before/after demo.
The asset is not tic-tac-toe. It is the loop:
Baseline → feedback → post-train → same-environment retest.
Use it for repeated, scoreable work: tool calls, structured extraction, domain instructions, and workflow Agents.
Task adaptation—not a broad capability claim.
token — The chunk of text a model reads and writes in — roughly three-quarters of a word — and the unit AI usage is billed in.
Why it matters
Agentic RL usually implies a cluster. This closes the baseline, feedback, post-train and retest loop on one desktop-class box with a 7.9B model, and both the tutorial and the framework are public.
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