E2e script - #10
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Signed-off-by: Sami Jaghouar <sami.jaghouar@gmail.com>
Signed-off-by: Sami Jaghouar <sami.jaghouar@gmail.com>
Signed-off-by: Sami Jaghouar <sami.jaghouar@gmail.com>
Signed-off-by: Sami Jaghouar <sami.jaghouar@gmail.com>
Signed-off-by: Sami Jaghouar <sami.jaghouar@gmail.com>
Signed-off-by: Sami Jaghouar <sami.jaghouar@gmail.com>
Signed-off-by: Sami Jaghouar <sami.jaghouar@gmail.com>
Signed-off-by: Sami Jaghouar <sami.jaghouar@gmail.com>
Signed-off-by: Sami Jaghouar <sami.jaghouar@gmail.com>
Signed-off-by: Sami Jaghouar <sami.jaghouar@gmail.com>
Signed-off-by: Sami Jaghouar <sami.jaghouar@gmail.com>
Signed-off-by: Sami Jaghouar <sami.jaghouar@gmail.com>
Signed-off-by: Sami Jaghouar <sami.jaghouar@gmail.com>
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leonardtang
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Jan 1, 2026
…main-dataset-mixing Support mixing tasks from multiple domains during training
samsja
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Mar 30, 2026
online-eval on swebench-verified
snimu
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Jun 4, 2026
- enabled_losses=None now validated as the full term list, so >1 echo term per env is caught at config time instead of at rollout time. [review #6] - loss_overrides keys validated against `losses`; non-echo overrides rejected. [#7] - warn (don't fail) when prompt-role echo is configured with renderer=None (MITO), where prompt_attribution is unavailable so it would silently no-op. [#8] - token_export: add echo_mask/echo_weight columns + export sequences trained only via echo (gate on loss_mask OR echo_mask). [#9] - doc notes: echo CE uses the rollout temperature (scale alpha to compensate, kept as-is); negative alpha is intentional (suppresses tokens). [#1, #10] - tests for the new config validators. Deferred to a follow-up pass (per the review): full per-sample primary routing / rl-disable [#2b] + the <=1-primary validation it enables [#5], and the multi-run losses fingerprint [#3]. Not run locally; ruff + py_compile clean. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
snimu
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Jun 5, 2026
…ht resolver - #1 reserve loss-term names {sft, opd} (any term) and `rl` (non-primary), so an overlay can't silently overwrite a training_mode dispatch core or the rl primary in the trainer registry. - #5 resolve the primary's advantage weight orchestrator-side: scale the per-token advantage by the advantage-weight's tau in process_group and drop adv_tau from the dppo_kl core / RLLossConfig. Now *any* primary core (dppo_kl or custom) gets the resolved advantage × tau — no per-core special-case. Bit-identical for the default tau=1.0. - #4 overlay trainability = non-None AND non-zero, so a zero-weight overlay (e.g. advantage-weighted with zero advantage) no longer keeps an otherwise-empty batch alive past the empty-batch guard. - #6 custom overlay weight resolver is group-aware: it now receives `WeightInputs{sample, rollouts}` (the full GRPO group) instead of a lone sample, so it can compute group-relative weights. - #10 document the overlay_mask/overlay_weight token-export columns (schema v2) in the configs skill. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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