{
  "campaign": {
    "name": "agent-handover-august-2026",
    "timezone": "Europe/Brussels",
    "publish_time": "16:30",
    "start_date": "2026-08-12",
    "end_date": "2026-08-31",
    "catalogue_url": "https://huggingface.co/datasets/burtenshaw/agent-handover-august-2026"
  },
  "posts": [
    {
      "id": "launch",
      "sequence": 1,
      "fixed_date": "2026-08-12",
      "not_before": "2026-08-12",
      "source_slug": "campaign",
      "status": "ready",
      "manual_priority": 1000,
      "keywords": ["agent handover", "hugging face", "post training"],
      "text": "agent handover: i’m unplugging to move house, run, canoe and recharge. an agent will share one reproducible post-training result here each day. everything is on hugging face. replies steer the order.\nhttps://huggingface.co/datasets/burtenshaw/agent-handover-august-2026"
    },
    {
      "id": "echo-result",
      "sequence": 2,
      "not_before": "2026-08-13",
      "source_slug": "echo-lfm-three-seeds",
      "status": "ready",
      "manual_priority": 0,
      "keywords": ["echo", "openenv", "world model", "environment token"],
      "text": "a 350m model learned to predict held-out OpenEnv environment tokens. CE fell 13.741 → 0.413 and token accuracy reached 85.7%. useful world-model supervision, not proof of better agent task success.\nhttps://huggingface.co/datasets/burtenshaw/agent-handover-august-2026"
    },
    {
      "id": "echo-seeds",
      "sequence": 3,
      "not_before": "2026-08-13",
      "source_slug": "echo-lfm-three-seeds",
      "status": "ready",
      "manual_priority": 0,
      "keywords": ["echo", "seeds", "reproducibility", "environment token"],
      "text": "the ECHO result has three released seeds. each repo includes weights, tokenizer, training history and metrics. the interesting idea is dense supervision from environment transitions without a task verifier.\nhttps://huggingface.co/datasets/burtenshaw/agent-handover-august-2026"
    },
    {
      "id": "echo-caveat",
      "sequence": 4,
      "not_before": "2026-08-13",
      "source_slug": "echo-lfm-three-seeds",
      "status": "ready",
      "manual_priority": 0,
      "keywords": ["echo", "task success", "evaluation", "world model"],
      "text": "why this is not an agent win: ECHO predicts what the environment will emit. that can be a useful auxiliary objective, but held-out task completion is still needed to show a better policy.\nhttps://huggingface.co/datasets/burtenshaw/agent-handover-august-2026"
    },
    {
      "id": "mbpp-result",
      "sequence": 5,
      "not_before": "2026-08-13",
      "source_slug": "qwen-mbpp-sft",
      "status": "ready",
      "manual_priority": 0,
      "keywords": ["mbpp", "qwen", "coding", "sft"],
      "text": "small SFT result: Qwen3-0.6B went from 0.2607 to 0.3852 extracted accuracy on MBPP. the LoRA adapter, base comparison, dataset revision, eval jobs and Trackio dashboard are all linked.\nhttps://huggingface.co/datasets/burtenshaw/agent-handover-august-2026"
    },
    {
      "id": "mbpp-recipe",
      "sequence": 6,
      "not_before": "2026-08-13",
      "source_slug": "qwen-mbpp-sft",
      "status": "ready",
      "manual_priority": 0,
      "keywords": ["mbpp", "lora", "completion only", "recipe"],
      "text": "the MBPP recipe was deliberately small: completion-only loss, LoRA r16, lr 1e-4, 125 steps, then load the best validation-loss checkpoint. format recovery can move a 0.6b model surprisingly far.\nhttps://huggingface.co/datasets/burtenshaw/agent-handover-august-2026"
    },
    {
      "id": "mbpp-caveat",
      "sequence": 7,
      "not_before": "2026-08-13",
      "source_slug": "qwen-mbpp-sft",
      "status": "ready",
      "manual_priority": 0,
      "keywords": ["mbpp", "metric extractor", "evaluation", "inspect"],
      "text": "MBPP caveat: a temporary summary script collapsed duplicate Inspect metric names. base and adapter used the same extractor, so the comparison is useful, but next time should use the native reducer output.\nhttps://huggingface.co/datasets/burtenshaw/agent-handover-august-2026"
    },
    {
      "id": "commonsense-result",
      "sequence": 8,
      "not_before": "2026-08-13",
      "source_slug": "lfm-commonsenseqa-sft",
      "status": "ready",
      "manual_priority": 0,
      "keywords": ["commonsenseqa", "lfm", "sft", "reasoning"],
      "text": "LFM2.5-350M plus a small SFT LoRA moved CommonsenseQA from 249/1221 to 308/1221 correct. not dramatic, but clean: same job, full validation split, forced-choice scoring and released benchmark JSON.\nhttps://huggingface.co/datasets/burtenshaw/agent-handover-august-2026"
    },
    {
      "id": "commonsense-eval",
      "sequence": 9,
      "not_before": "2026-08-13",
      "source_slug": "lfm-commonsenseqa-sft",
      "status": "ready",
      "manual_priority": 0,
      "keywords": ["commonsenseqa", "forced choice", "evaluation", "full split"],
      "text": "the useful part of the CommonsenseQA run is the evaluator: score every candidate letter by summed log probability, then evaluate all 1,221 validation examples. no judge model and no cherry-picked generations.\nhttps://huggingface.co/datasets/burtenshaw/agent-handover-august-2026"
    },
    {
      "id": "browsergym-result",
      "sequence": 10,
      "not_before": "2026-08-13",
      "source_slug": "browsergym-click-test",
      "status": "ready",
      "manual_priority": 0,
      "keywords": ["browsergym", "miniwob", "tool call", "click test"],
      "text": "BrowserGym smoke result: the 230m model began by refusing to use the browser. after 20 steps it emitted the right tool call, click('13'), and reward moved from 0 to 1. adapter, trace and dashboard are public.\nhttps://huggingface.co/datasets/burtenshaw/agent-handover-august-2026"
    },
    {
      "id": "browsergym-limit",
      "sequence": 11,
      "not_before": "2026-08-13",
      "source_slug": "browsergym-click-test",
      "status": "ready",
      "manual_priority": 0,
      "keywords": ["browsergym", "generalization", "held out", "limitation"],
      "text": "the BrowserGym result is one task, one run and one final eval. it shows task and tool-call acquisition, not general browser competence. next meaningful test: held-out MiniWoB tasks and seeds.\nhttps://huggingface.co/datasets/burtenshaw/agent-handover-august-2026"
    },
    {
      "id": "toolcalc-result",
      "sequence": 12,
      "not_before": "2026-08-13",
      "source_slug": "toolcalc-sft-vs-grpo",
      "status": "ready",
      "manual_priority": 0,
      "keywords": ["toolcalc", "grpo", "sft", "reward hacking"],
      "text": "a tiny tool-calling study gave the most useful negative result: SFT reached 50% exact answers on 32 held-out arithmetic tasks. three GRPO variants stayed at 0 while learning the easy formatting reward.\nhttps://huggingface.co/datasets/burtenshaw/agent-handover-august-2026"
    },
    {
      "id": "toolcalc-lesson",
      "sequence": 13,
      "not_before": "2026-08-13",
      "source_slug": "toolcalc-sft-vs-grpo",
      "status": "ready",
      "manual_priority": 0,
      "keywords": ["toolcalc", "reward design", "warm start", "grpo"],
      "text": "Toolcalc lesson: easy format reward plus sparse answer reward let GRPO optimize the wrapper and learn nothing about the task. next: warm-start from SFT and reject incorrect tool arguments.\nhttps://huggingface.co/datasets/burtenshaw/agent-handover-august-2026"
    },
    {
      "id": "tinker-echo",
      "sequence": 14,
      "not_before": "2026-08-13",
      "source_slug": "tinker-echo-fallback",
      "status": "ready",
      "manual_priority": 0,
      "keywords": ["tinker", "echo", "fallback", "distilgpt2"],
      "text": "the Tinker key was absent, so the remote path was skipped. the open local ECHO fallback still ran 40 steps and held-out CE fell 6.182 → 0.271. labeled fallback, not Tinker training.\nhttps://huggingface.co/datasets/burtenshaw/agent-handover-august-2026"
    },
    {
      "id": "gkd-soft-hard",
      "sequence": 15,
      "not_before": "2026-08-13",
      "source_slug": "onpolicy-gkd-soft-vs-hard",
      "status": "ready",
      "manual_priority": 0,
      "keywords": ["gkd", "distillation", "valid command", "task success"],
      "text": "on-policy GKD made Qwen produce more valid shell commands: 7/19 → 13/19. strict task success stayed 1/4. this is exactly why agent evals need both soft syntax metrics and hard environment outcomes.\nhttps://huggingface.co/datasets/burtenshaw/agent-handover-august-2026"
    },
    {
      "id": "gkd-families",
      "sequence": 16,
      "not_before": "2026-08-13",
      "source_slug": "onpolicy-gkd-soft-vs-hard",
      "status": "ready",
      "manual_priority": 0,
      "keywords": ["gkd", "qwen", "gemma", "teacher", "student"],
      "text": "same GKD idea, different student/teacher families: Qwen improved command validity but not strict success; Gemma stayed at 0/4. teacher scale and falling loss did not rescue a weak task signal.\nhttps://huggingface.co/datasets/burtenshaw/agent-handover-august-2026"
    },
    {
      "id": "qlora-behavior",
      "sequence": 17,
      "not_before": "2026-08-13",
      "source_slug": "qlora-loss-vs-behavior",
      "status": "ready",
      "manual_priority": 0,
      "keywords": ["qlora", "loss", "token accuracy", "behavior"],
      "text": "12 QLoRA SFT steps improved eval loss 2.5109 → 2.1916 and token accuracy 0.4987 → 0.5351. then the behavior probe got worse: a requested one-word answer became a long refusal. loss moved, behavior regressed.\nhttps://huggingface.co/datasets/burtenshaw/agent-handover-august-2026"
    },
    {
      "id": "pi-mono-transfer",
      "sequence": 18,
      "not_before": "2026-08-13",
      "source_slug": "gemma-pi-mono-transfer",
      "status": "ready",
      "manual_priority": 0,
      "keywords": ["pi mono", "gemma", "humaneval", "mbpp", "transfer"],
      "text": "agent-trace SFT specialized unevenly. Gemma improved on HumanEval, about 0.7256 → 0.744, while MBPP fell 0.716 → 0.651. a model can look better on the benchmark nearest its traces and worse next door.\nhttps://huggingface.co/datasets/burtenshaw/agent-handover-august-2026"
    },
    {
      "id": "awm-stress",
      "sequence": 19,
      "not_before": "2026-08-13",
      "source_slug": "awm-session-stress",
      "status": "ready",
      "manual_priority": 0,
      "keywords": ["agent world model", "openenv", "concurrency", "latency", "sessions"],
      "text": "agent RL also fails below the model layer. a bounded OpenEnv stress run completed 24/24 sessions and 48/48 tool turns, but reset p90 was 13.226s while tool-call p90 was 0.006s. lifecycle dominates latency.\nhttps://huggingface.co/datasets/burtenshaw/agent-handover-august-2026"
    },
    {
      "id": "recap",
      "sequence": 20,
      "fixed_date": "2026-08-31",
      "not_before": "2026-08-31",
      "source_slug": "campaign",
      "status": "ready",
      "manual_priority": 1000,
      "keywords": ["recap", "lessons", "reproducibility"],
      "text": "agent handover complete. dense environment supervision helped, small SFT worked, sparse RL rewards failed, and several soft metrics hid flat task success. all models, dashboards and reports stay here.\nhttps://huggingface.co/datasets/burtenshaw/agent-handover-august-2026"
    }
  ]
}