We cut a real coding-agent session, logged from a current frontier agent, at 5 points, from 0.9M to 7.4M tokens, and kept everything before each cut in context. Each time Manta writes the next turn on its own: its reasoning and its message, up to the point where it runs its next tool.
Close to 1M tokens the frontier agent reached its limits: its context was compacted into a summary at 786,556 tokens, and 13 times by the last cut. Manta reads the whole session at every cut, with room to look back and ahead.
One sample per cut, unedited. IPs, keys, IDs, paths and internal names read [REDACTED]; nothing else is changed.
The frontier agent ran this session live. Close to 1M tokens it reached its limits and from then on worked from summaries. Manta reads every cut whole, with room to look back and ahead.
gpu: 10 %, 908 MiB
The re-test (task-following + identity) is running on the GPU right now (that's the 16 procs).
identity3 is done (val 1.32) — GPU's idle, so the re-test should be finished too.
It was finished: the results came back seconds later.
No message. It runs its next command: Bash “Waves 3+4 training results”
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:
Manta says what doesn't add up before it checks.
DPO composite saved. Executing the post-train sequence — HF upload and the vLLM install both launch now in parallel:
DPO saved. Three parallel actions: upload (protect the artifact), serve on 8080 for your tunnel, strict gate:
ssh -p [REDACTED] -i [REDACTED]/.ssh/id_ed25519 root@[REDACTED] 'cd /root/[REDACTED] && echo "identity3 final: $([ -f train/runs/[REDACTED]/sft_final.pt ] && echo YES || echo no)"; echo "train tail:"; grep -E "saved|val_loss" identity3_train.log 2>/dev/null | tail -2; echo "python procs: $(pgrep -fc "[p]ython")"; echo "gpu: $(nvidia-smi --query-gpu=utilization.gpu,memory.used --format=csv,noheader)"' 2>/dev/null
identity3 final: YES
train tail:
VAL {'step': 38, 'epoch': 2.959, 'val_loss': 1.3226, 'best': 1.3226}
saved /root/[REDACTED]/train/runs/[REDACTED]/sft_final.pt (best val 1.3226)
python procs: 16
gpu: 10 %, 908 MiBidentity3 is done (val 1.32) — GPU's idle, so the re-test should be finished too. Reading it:identity3 finished — and a good sign: val 1.3226, better than identity2's 1.34 and near the pre-persona sft_best (1.295), so the gentler pass degraded general ability much less. The re-test (task-following + identity) is running on the GPU right now (that's the 16 procs). I'll have the verdict — does she write hello-world cleanly *and* still know she's [REDACTED] — in a minute or two when the watcher fires. Holding for it.~/.local/bin/[REDACTED] (standalone). Let me use the full path and check the exec flags:export PATH="$HOME/.local/bin:$PATH"; [REDACTED] --version 2>&1 | head -1; echo "=== exec help ==="; [REDACTED] exec --help 2>&1 | sed -n '1,50p'
[REDACTED] 0.145.0
=== exec help ===
Run [REDACTED] non-interactively
Usage: [REDACTED] exec [OPTIONS] [PROMPT]
[REDACTED] exec [OPTIONS] <COMMAND> [ARGS]
Commands:
resume Resume a previous session by id or pick the most recent with --last
review Run a code review against the current repository
help Print this message or the help of the given subcommand(s)
Arguments:
[PROMPT]
Initial instructions for the agent. If not provided as an argument (or if `-` is used),
instructions are read from stdin. If stdin is piped and a prompt is also provided, stdin
is appended as a `<stdin>` block
Options:
-c, --config <key=value>
Override a configuration value that would otherwise be loaded from `~/.[REDACTED]/config.toml`.
Use a dotted path (`foo.bar.baz`) to override nested values. The `value` portion is parsed
as TOML. If it fails to parse as TOML, the raw string is used as a literal.
Examples: - `-c model="[REDACTED]"` - `-c 'sandbox_permissions=["di[REDACTED]"]'` - `-c
shell_environment_policy.inherit=all`
--enable <FEATURE>
Enable a feature (repeatable). Equivalent to `-c features.<name>=true`
--disable <FEATURE>
Disable a feature (repeatable). Equivalent to `-c features.<name>=false`
--strict-config
Error out when config.toml contains fields that are not recognized by this version of
[REDACTED]
-i, --image <FILE>...
Optional image(s) to attach to the initial prompt
-m, --model <MODEL>
Model the agent should use
--oss
Use open-source provider
--local-provider <OSS_PROVIDER>
Specify which local provider to use (lmstudio or ollama). If not specified with --oss,
will use config default or show selection
-p, --profile <CONFIG_PROFILE_V2>
Layer $[REDACTED]/<name>.config.toml on top of the base user configexport PATH="$HOME/.local/bin:$PATH"; [REDACTED] exec --help 2>&1 | sed -n '50,110p'
cat [REDACTED]/tasks/bcbadj7uv.output 2>/dev/null | grep -vE "Welcome|Have fun|AI agents|READ /etc"
(Bash completed with no output)
ssh -p [REDACTED] -o ConnectTimeout=60 root@[REDACTED] 'echo "=== getdata ==="; tail -10 /root/getdata.log 2>/dev/null; echo "=== model dl ==="; tail -2 /root/dl.log 2>/dev/null; du -sh /root/.hf_home 2>/dev/null; ls -la /root/[REDACTED]/*.txt 2>/dev/null' 2>&1 | grep -vE "Welcome|Have fun|AI agents|READ /etc"
=== getdata ===
builder_instance = load_dataset_builder(
^^^^^^^^^^^^^^^^^^^^^
File "/venv/main/lib/python3.12/site-packages/datasets/load.py", line 1323, in load_dataset_builder
dataset_module = dataset_module_factory(
^^^^^^^^^^^^^^^^^^^^^^^
File "/venv/main/lib/python3.12/site-packages/datasets/load.py", line 1215, in dataset_module_factory
raise e1 from None
File "/venv/main/lib/python3.12/site-packages/datasets/load.py", line 1175, in dataset_module_factory
raise RuntimeError(f"Dataset scripts are no longer supported, but found {filename}")
RuntimeError: Dataset scripts are no longer supported, but found pg19.py
=== model dl ===
Fetching 16 files: 6%|▋ | 1/16 [00:00<00:02, 7.12it/s]
Fetching 16 files: 12%|█▎ | 2/16 [00:00<00:01, 8.23it/s]
Fetching 16 files: 44%|████▍ | 7/16 [00:25<00:37, 4.16s/it]
Fetching 16 files: 50%|█████ | 8/16 [00:26<00:28, 3.57s/it]
Fetching 16 files: 100%|██████████| 16/16 [00:26<00:00, 1.68s/it]
MODEL_AT /root/.hf_home/hub/[REDACTED]/snapshots/[REDACTED]
19G /root/.hf_homedatasets-5.0 script block on deepmind/pg19. Fixing with a parquet-only source:getdata failed — deepmind/pg19 ships a loading script, which datasets v4 dropped. Probing which sources actually load on this stack:ssh -p [REDACTED] -o ConnectTimeout=180 root@[REDACTED] 'export HF_HOME=/root/.hf_home; timeout 600 /venv/main/bin/python - <<PY 2>&1 | grep -vE "^Downloading|^Resolving|it/s\]|^ *$"
from datasets import load_dataset
cands = [
("emozilla/pg19", None, "text"),
("deepmind/pg19", None, "text"),
("storytracer/US-PD-Books", None, "text"),
("HuggingFaceFW/fineweb-edu", "sample-10BT", "text"),
("bigcode/the-stack-smol", None, "content"),
("codeparrot/codeparrot-clean-valid", None, "content"),
]
for name, cfg, col in cands:
try:
kw = dict(split="train", streaming=True)
if cfg: kw["name"]=cfg
d = load_dataset(name, **kw)
ex = next(iter(d))
n = len(ex.get(col) or "")
print(f"OK {name:<38} col={col} first_doc={n} chars")
except Exception as e:
print(f"FAIL {name:<38} {type(e).__name__}: {str(e)[:90]}")
PY' 2>&1 | grep -vE "Welcome|Have fun|AI agents|READ /etc"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.
Also 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.
```
wave 3 train ~03:30, at 5e-5, robust budget
wave 3 done ~05:30, artifacts auto-pulled
wave 4 chains immediately
morning four adapters, full stats, LR ablation across 5e-5/7e-5, your call
```ssh [REDACTED] "date -u -d '+2 hours' '+ your local %H:%M' W3=/root/[REDACTED]/grpo/runs/ppo_run2/wave3 echo \" driver \$(pgrep -f '[p]po_wave1.sh'|head -1||echo DEAD) | rollout \$(pgrep -f '[a]gentic_rollout'|head -1||echo -)\" echo \" wave3 episodes: \$([ -f \$W3/episodes.jsonl ] && wc -l < \$W3/episodes.jsonl || echo 0)/48\" grep -oE 'term=[a-z_]+' \$W3/rollout.log 2>/dev/null | sort | uniq -c | sort -rn | head -5 | sed 's/^/ /' echo ' rewards:'; grep -oE 'reward=[0-9.]+' \$W3/rollout.log 2>/dev/null | sed 's/reward=//' | tr '\n' ' ' | fold -w 116 | sed 's/^/ /' echo tail -1 /root/[REDACTED]/grpo/runs/ppo_run2/driver.log | sed 's/^/ /'" 2>&1 | grep -viE "[REDACTED]|agents|READ|Have fun"
your local 07:13
driver | rollout
wave3 episodes: 48/48
22 term=finish
14 term=judge_stuck
9 term=loop
2 term=stalled_no_tool_calls
1 term=walltime
rewards:
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
.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
3451603 0.0 0.0 0.0 0.7305099977064698 0.0 0.2537749081726354 0.0 0.5048634105960265 0.9824999999999999 1.0 0.982499
9999999999 0.0 0.9824999999999999 1.0 0.0 0.9475 0.0 0.7598613626075147 0.0 0.0 0.0
[03:42:06] === all 2 waves completessh [REDACTED] "for w in 3 4; do W=/root/[REDACTED]/grpo/runs/ppo_run2/wave\$w echo \" === wave \$w ===\" grep -m1 'LR OVERRIDE' \$W/train.log 2>/dev/null | sed 's/^/ /' /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/^/ /' done ls -d /root/[REDACTED]/[REDACTED]* 2>/dev/null | sed 's/^/ policy: /' tail -3 /root/[REDACTED]/grpo/runs/ppo_run2/driver.log | sed 's/^/ /'" 2>&1 | grep -viE "[REDACTED]|agents|READ|Have fun"
dpo/ directory is being written (4 files so far; the 24GB safetensors is the slow part). Final stage stats: margin +220, acc 1.00, disp −0.031 — chosen side ended ~3% more likely per token than the reference, rejected suppressed, zero displacement, guard never fired. A few minutes for the write, then the watcher fires and I move straight to: HF upload + vLLM serve on 8080 + strict gate + the verdict roll.{
"file_path": "[REDACTED]/tasks/bmkzrgyvu.output"
}1 [dpo] epoch 3 step 13 loss 0.0000 margin +204.984 acc 1.00 disp -0.0268 2 [dpo] epoch 3 step 14 loss 0.0000 margin +225.186 acc 1.00 disp -0.0299 3 [dpo] epoch 3 step 15 loss 0.0000 margin +219.944 acc 1.00 disp -0.0315 4 [dpo] saved -> /w/[REDACTED]/train/dpo 5 chat_template.jinja 6 config.json 7 configuration_[REDACTED] 8
ssh -p [REDACTED] root@[REDACTED] "ps -p [REDACTED] > /dev/null 2>&1 && echo trainer-STILL-alive || echo trainer-exited; nvidia-smi --query-gpu=memory.used --format=csv,noheader; source /venv/main/bin/activate && HF_TOKEN=[REDACTED] nohup hf upload [REDACTED]/[REDACTED] /w/[REDACTED]/train/dpo mp_dpo --private > /w/upload_dpo.log 2>&1 & nohup bash -c 'source /venv/main/bin/activate && uv pip install vllm==0.26.0 flashinfer-python==0.6.17 2>&1 | tail -2' > /w/vllm_install.log 2>&1 & echo launched-upload-and-install" 2>/dev/null | grep -v READ