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Live training visualization + aggressive reward shaping to prevent 'do nothing' collapse
Server: - Add /train/plot (auto-refreshing HTML) and /train/plot.png (matplotlib curves) - Add /train/live JSON streaming of reward/loss/tool metrics per log step - Add /outputs/ls, /outputs/file, /outputs/archive download endpoints - Add /train/push_to_hub for durable artifact persistence to HF Hub - /train/pilot now takes max_completion_length (default 512) and logging_steps (default 5) Training: - ReleaseOpsGRPOEnv tracks tool_calls_made and tool_calls_failed per rollout - reward_func adds layered shaping: NON_TERMINATION_PENALTY = -0.5 (break clipping collapse) SILENT_ROLLOUT_PENALTY = -0.4 (make 'do nothing' the worst non-terminal option) FORMAT_BONUS_ON_ACTION = +0.05 (keep exploration alive) Resulting ordering pushes policy toward any terminal action before correctness. - LiveLogHistoryCallback persists log_history on every on_log event so /train/live and /train/plot show real-time data. Dataset: - System prompt rewritten from 'Use tools sparingly' (discour…4dde8b9 -
Deploy ReleaseOps Arena GPU Space
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