{
 "id": "session1-5.0M",
 "context_tokens": 4999341,
 "context_messages": 19623,
 "cut": "at a turn boundary, right after a tool result; Manta writes the next assistant turn",
 "sampling": {
  "temperature": 1.0,
  "top_p": 0.95,
  "max_new_tokens": 2048,
  "samples": 1
 },
 "context_tail": [
  {
   "role": "assistant",
   "thinking": "",
   "text": "**Yes — every fix is verified live on the box**, not just written. The audited list:\n\n| fix | verified |\n|---|---|\n| bf16 adapters (kills the [REDACTED] segfault) | ✅ |\n| 30k sequence cap (kills the backward OOM) | ✅ |\n| **LR clamp → 5e-5** (loud override print) | ✅ |\n| **tail-robust KL budget** (±2 clamp, reporting stays raw) | ✅ shipped in time for wave-3's train |\n| recompute pi_old (wave-0 capture semantics) | ✅ |\n| live progress + exit-code capture | ✅ |\n| smoke gate before every epoch | ✅ (running driver copy) |\n| resume-at-stage, Dynamo off, fresh Triton cache | ✅ |\n| ctx 40k / compact 30k rollout limits | ✅ |\n| canonical local copies match (incl. driver at 5e-5 for future launches) | ✅ |\n\nWave 3 is at 11/48 episodes; its smoke and train will print `LR OVERRIDE → 5e-5` as confirmation, and with the robust budget the expectation is **17–18 of 18 steps** instead of donating a third of the batch to a noisy estimator.\n\nAlso logged the pgrep lesson to memory so the next session inherits it — the false alarm cost us nothing tonight, but only because I chased it instead of acting on it.\n\n```\nwave 3 train    ~03:30, at 5e-5, robust budget\nwave 3 done     ~05:30, artifacts auto-pulled\nwave 4          chains immediately\nmorning         four adapters, full stats, LR ablation across 5e-5/7e-5, your call\n```",
   "tool_calls": []
  },
  {
   "role": "user",
   "thinking": "",
   "text": "update",
   "tool_calls": []
  },
  {
   "role": "assistant",
   "thinking": "",
   "text": "",
   "tool_calls": [
    {
     "name": "Bash",
     "arguments": {
      "command": "ssh [REDACTED] \"date -u -d '+2 hours' '+  your local %H:%M'\nW3=/root/[REDACTED]/grpo/runs/ppo_run2/wave3\necho \\\"  driver \\$(pgrep -f '[p]po_wave1.sh'|head -1||echo DEAD) | rollout \\$(pgrep -f '[a]gentic_rollout'|head -1||echo -)\\\"\necho \\\"  wave3 episodes: \\$([ -f \\$W3/episodes.jsonl ] && wc -l < \\$W3/episodes.jsonl || echo 0)/48\\\"\ngrep -oE 'term=[a-z_]+' \\$W3/rollout.log 2>/dev/null | sort | uniq -c | sort -rn | head -5 | sed 's/^/    /'\necho '  rewards:'; grep -oE 'reward=[0-9.]+' \\$W3/rollout.log 2>/dev/null | sed 's/reward=//' | tr '\\n' ' ' | fold -w 116 | sed 's/^/    /'\necho\ntail -1 /root/[REDACTED]/grpo/runs/ppo_run2/driver.log | sed 's/^/  /'\" 2>&1 | grep -viE \"[REDACTED]|agents|READ|Have fun\"",
      "description": "Wave 3 rollout status",
      "timeout": "300000"
     }
    }
   ]
  },
  {
   "role": "tool",
   "content": "your local 07:13\n  driver  | rollout \n  wave3 episodes: 48/48\n         22 term=finish\n         14 term=judge_stuck\n          9 term=loop\n          2 term=stalled_no_tool_calls\n          1 term=walltime\n  rewards:\n    0.0 0.0 0.34387499999999993 0.0 0.0 0.0 0.9824999999999999 0.5163875 0.0 0.9840182648401826 1.0 0.9824999999999999 0\n    .0 0.0 0.35 0.0 0.0 0.0 0.9475 0.0 0.9824999999999999 0.9824999999999999 0.44019442066210046 0.0 0.0 0.0 0.925659380\n    3451603 0.0 0.0 0.0 0.7305099977064698 0.0 0.2537749081726354 0.0 0.5048634105960265 0.9824999999999999 1.0 0.982499\n    9999999999 0.0 0.9824999999999999 1.0 0.0 0.9475 0.0 0.7598613626075147 0.0 0.0 0.0 \n  [03:42:06] === all 2 waves complete"
  }
 ],
 "continuation": {
  "role": "assistant",
  "thinking": "The training run finished at 07:13 local time, but the driver and rollout processes have disappeared. The logs show wave 3 episodes completed at 03:42:06, but I'm not seeing the corresponding rollout lines in the output, and there's a discrepancy between the expected values and what's actually there.\n\nLooking at the full chain, it seems the driver completed the two waves but something went wrong with the wave 3 and wave 4 training — the train process isn't showing up, and wave 4 episodes aren't being generated. The driver log shows it finished, but the wakeup script didn't trigger a new launch since no driver was detected. There's also the possibility that my 5e-5 checkpoint either failed to write or wrote to an unexpected location.\n\nI need to pull the full driver log to understand what happened with wave 4, and check whether wave 3's train completed successfully and where wave 4 stands.",
  "text": "Both waves are done, but I need to read this carefully — the log tail says **\"all 2 waves complete\"** and yet only wave 3 has episodes. Let me get the full picture:",
  "tool_calls": []
 },
 "original_next": {
  "role": "assistant",
  "thinking": "Wait, the driver actually exited after both waves finished at 05:42 local time, and it's been sitting idle for the past hour and a half — so waves 3 and 4 both completed successfully, not still running.\n\nBut here's the real problem: the wakeup chain that was supposed to fire every 40 minutes never triggered. I scheduled the next wakeup for 02:29, but there's no record of it actually firing during the entire idle period from 02:30 to 07:13. The wakeups might have died when I called [REDACTED](stop) earlier, and even though I re-armed it three times after that, the last scheduled wakeup at 02:29 never executed — which means the wave-artifact pull chain never ran. I need to check what artifacts actually exist for waves 3 and 4 to see what got pulled.\n\nLooking at the wave-3 rollout numbers, I'm tallying up the solve rates: roughly 16 solves out of 48 attempts (33%), with terminations split across 22 finishes, 14 judge_stuck cases (which is concerning), 9 loop timeouts, and 1 walltime hit. The mean solve rate comes to around 0.367.\n\nRight now my priorities are: confirm both wave-3 and wave-4 training actually completed with their stats, verify the LR override fired and the budget behavior held up, then immediately pull both waves' artifacts and push them to HF. The GPU's been idle since 05:42.\n\nThe wakeup chain failed again after burning ~1.5 hours and $1.9 in compute—better than the 6-hour disaster, but still worth investigating later. All four adapters exist now (w0, w2, w3, w4), and the user said to run eval after all waves finish. Since waves are done and nothing's running, I could start the holdout eval right now on the idle GPU—that aligns with their principle that idle compute is wasted money, and it'd have the decision data ready when they wake up.\n\nFor the eval design: I need to compare the final policy ([REDACTED]) against the baseline ([REDACTED]) on the 22 held-out instances with K=2 rollouts. That's roughly 44 episodes per policy, taking 60-75 minutes each, so about 2.5 hours total—a solid morning deliverable. It's read-only evaluation with no training, exactly what they asked for. The w4 policy is built (w3 got cleaned up), so I can pull the artifacts and launch the first eval leg right away.\n\nFor the reward setup during eval, I should disable teacher shaping and use only the functional reward to keep the comparison clean—the training included teacher shaping, but for a pure functional eval, that's the right call. Then run the same holdout tasks against [REDACTED] as leg 2. Since eval is the equivalent of paid compute (not wasted), this is justified.\n\nNow I need to handle the holdout tasks: I'll generate the holdout list from the pick_tasks logic using the frozen hash bucket and intersecting with usable images, then write a small script to emit that list. After that, I can run steps 1 and 2 together in one command before setting up the eval.\nBoth processing waves finished at 05:42 and the system has been idle for 1.5 hours since the wakeup chain failed again, so I'm securing everything now and then putting the GPU back to work.",
  "text": "",
  "tool_calls": [
   {
    "name": "Bash",
    "arguments": {
     "command": "ssh [REDACTED] \"for w in 3 4; do W=/root/[REDACTED]/grpo/runs/ppo_run2/wave\\$w\necho \\\"  === wave \\$w ===\\\"\ngrep -m1 'LR OVERRIDE' \\$W/train.log 2>/dev/null | sed 's/^/  /'\n/venv/main/bin/python -m json.tool \\$W/train/train_stats.json 2>/dev/null | grep -E 'opt_steps_total|stopped_early|clip_frac_active|explained_var|kl_vs_pi_old|mean_episode' | sed 's/^/  /'\ndone\nls -d /root/[REDACTED]/[REDACTED]* 2>/dev/null | sed 's/^/  policy: /'\ntail -3 /root/[REDACTED]/grpo/runs/ppo_run2/driver.log | sed 's/^/  /'\" 2>&1 | grep -viE \"[REDACTED]|agents|READ|Have fun\"",
     "description": "Waves 3+4 training results",
     "timeout": "300000"
    }
   }
  ]
 },
 "original_agent": {
  "kind": "a current frontier agent",
  "context_compactions_before_cut": 8
 }
}