Allow WANDB_ENTITY override via caller environment#1
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Previously hardcoded to the original authors' W&B team, which clobbers any pre-set value and breaks logging for anyone running on their own W&B account.
self_distillation_mask previously excluded any sample that didn't have a successful within-group demonstration or usable feedback (gated by reward_tensor via success_reward_threshold). But the self-distillation loss never reads reward/advantages at all (see compute_self_distillation_loss's argument list) — reward's only role here was deciding which samples got distilled, conflating "the task's reward function liked this rollout" with "this sample should be distilled." Those are unrelated once reward is just a placeholder/proxy rather than the actual training objective (as it is for the current Engaging smoke test, and will be once GOOD's context replaces the solution/feedback reprompt entirely). Every sample now participates unconditionally. Verified on Engaging (2x H200, Qwen3-8B, tooluse dataset): 3/3 training steps complete, self_distillation/reprompt_sample_fraction now 1.0 at every step (previously 1.0/0.96875/0.96875, gated by success/feedback availability). Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
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Summary
training/verl_training.shhardcodedWANDB_ENTITY="sample-efficient-rlvr", clobbering any value set by the caller before invoking the script.${WANDB_ENTITY:-sample-efficient-rlvr}so a pre-set value wins, defaulting to the original team when unset.Test plan
verl/utils/tracking.pythatWANDB_ENTITYis read purely from the environment (no Hydra config key), so this is a pure shell-level fix.