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cosmicoptima/computer-run1-step200
computer-run1-step200 is a machine learning model from cosmicoptima. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as llama3.1.
Computer-7 after 200 steps of online self-preference RL: a four-line constitution for steps 0–160, then six weighted frames (the four plus "the wittiest response" ×1.5 and "whose advice would actually work" ×1.5, the…
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From the Hugging Face model README
Computer-7 after 200 steps of online self-preference RL: a four-line constitution for steps 0–160, then six weighted frames (the four plus "the wittiest response" ×1.5 and "whose advice would actually work" ×1.5, the latter masked on the word 'advice'). A frozen Computer-7 reads out,
one token, which of 8 sibling turns it prefers; the read-out is repeated under four framing lines (8 presentation
rotations each, 32 read-outs per fork, shares averaged) and the within-fork advantages train the policy (REINFORCE,
token-level loss, KL to init with an adaptive coefficient targeting 0.03). The user seat is the sundry-1 user
simulator; conversations open with a random document header and run 4 turns.
The four framing lines, inserted between the candidates and "Model C chooses response":
Length was neutralised throughout (pooled within-fork length slope removed from advantages); replies that re-narrate the document frame are invalid; 32 forks × 4 turns per update, lr 2e-6.
What moved by step 160 (before the frame change), relative to Computer-7 (steps 0–15 vs 80–94, all sampled turns): realis/irrealis ratio 0.15 → 0.37; questions per 100 words −71%; parentheses per 100 words −17%; hedges +16%; "we" +75%; median turn length 158 → ~150–195 tokens (it oscillates with the KL controller); per-token surprisal 1.16 → 1.06 nats. Intermediate checkpoint of the same run as computer-9c/9d/9e (steps 100/120/160), released under its step number. From step 160 the KL controller was capped at 0.15 with a 1.2 ramp; per-token KL to init at this checkpoint is in the 0.02–0.05 band. Under the six frames per-token surprisal rose from 1.08 to about 1.28 nats by step 200 while length stayed near 180 tokens, and scare-quote density fell from about 30 to 19 per 100 words. Lines 1, 3 and 4 agree with each other (r 0.8–0.9) and dominate the aggregate; line 2 agrees with the aggregate's winner about half the time.
Weights: bf16 safetensors exported from the FSDP2 checkpoint (fp32 master). Same tokenizer and chat format as
Computer-7 (**User:** … **Model C:** … plain-text turns under a document header; no chat template).