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Rewards and correctness are shares of the 256 held-out prompts; every checkpoint uses the same prompts and the same sampling seed, so two checkpoints differ only by the policy.","modelSha256":"67600098679443bf87c78f0f3ef1a1399ae51642b3e9608517105d7831219d5e","modelFile":"media/school/attention/model.json","recipe":{"batch":32,"lr":0.001,"evalPrompts":256,"evalSeed":4242,"evalSampleSeed":99,"examples":6,"steps":800,"every":25,"seeds":[1,2,3],"optimiser":"REINFORCE: advantage = reward - batch mean reward; loss = -(1/B) sum_b A_b sum_k log p(a_bk); Adam, no weight decay, gradient clip 1","maxAnswer":3},"task":{"about":"The task: a who prompt in the corpus generator's grammar, opening with a decoy sentence (a local sentence, usually naming a guide), then the deed, then 0 or 1 distractor, cut at \"?\". The answer is at most 3 words, ended by \".\"; it is correct when it is exactly the guide who did the deed and then \".\". The truth is the generator's exact next-word odds after the prompt (corpus-grammar.mjs), which put probability 1 on that guide for every prompt here. decoyShare is the share of the 256 held-out prompts whose first word is a guide who did not do the deed (a policy answering \"the first name\" is wrong there). 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REINFORCE against each checker for 800 steps, batch 32, learning rate 0.001, seeds 1, 2, 3 (prompt stream 10 + seed, sampling stream 20 + seed). start is the step-0 score; end is over seeds at step 800; reward is the run's own checker on the held-out prompts; peakCorrect is each seed's best checkpoint; firstStepCorrectBelow is the first checkpoint where correctness is under half its step-0 value (null if never). curveMean averages the seeds at every 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