Datasets:

SlowGuess
/

abforge-isambard-transfer

Task 1 preprocess:
- /Users/xucai/Project/verl_with_llm-as-judge-master/verl_proj/examples/data_preprocess/abforge_task1_rl.py
Task 2 preprocess:
- /Users/xucai/Project/verl_with_llm-as-judge-master/verl_proj/examples/data_preprocess/abforge_task2_rl.py
Task 1 local judge reward:
- /Users/xucai/Project/verl_with_llm-as-judge-master/reward_part/task1_call_api.py
- /Users/xucai/Project/verl_with_llm-as-judge-master/reward_part/task1_rw.py
Task 2 local judge reward:
- /Users/xucai/Project/verl_with_llm-as-judge-master/reward_part/task2_call_api.py
- /Users/xucai/Project/verl_with_llm-as-judge-master/reward_part/task2_rw.py
Task 1 Azure/API judge reward:
- /Users/xucai/Project/verl_with_llm-as-judge-master/reward_part/azure_task1_judge/task1_azure_reward.py
Task 2 Azure/API judge reward:
- /Users/xucai/Project/verl_with_llm-as-judge-master/reward_part/azure_task2_judge/task2_azure_reward.py

Tracked launchers:

/Users/xucai/Project/verl_with_llm-as-judge-master/verl_proj/examples/grpo_trainer/run_abforge_qwen3_8b_task1_hf.sh
/Users/xucai/Project/verl_with_llm-as-judge-master/verl_proj/examples/grpo_trainer/run_abforge_qwen3_8b_task1_vllm.sh
/Users/xucai/Project/verl_with_llm-as-judge-master/verl_proj/examples/grpo_trainer/run_abforge_qwen3_8b_task1_vllm_azurejudge.sh
/Users/xucai/Project/verl_with_llm-as-judge-master/verl_proj/examples/grpo_trainer/run_abforge_qwen3_8b_hf.sh
/Users/xucai/Project/verl_with_llm-as-judge-master/verl_proj/examples/grpo_trainer/run_abforge_qwen3_8b_vllm.sh
/Users/xucai/Project/verl_with_llm-as-judge-master/verl_proj/examples/grpo_trainer/run_abforge_qwen3_8b_vllm_azurejudge.sh
/Users/xucai/Project/verl_with_llm-as-judge-master/verl_proj/examples/grpo_trainer/run_abforge_qwen3_8b_vllm_qwen3_32b_judge.sh

2. Misha Path Map

The user works on Yale Misha.

There are three path classes that matter:

A. Home

/gpfs/radev/home/yz979

Current role:

shell login location
not used for training artifacts
do not write caches, logs, checkpoints, or model downloads here

B. Project

/gpfs/radev/project/cohan/yz979/xucai/Abforge_Training

Recommended variable:

export CODE_ROOT=/gpfs/radev/project/cohan/yz979/xucai/Abforge_Training

This is the canonical code / repo root on Misha.

What lives here now:

.git/
readme.md
reward_part/
verl_proj/
slurm/
small data/ directory inside repo root
reference PDF

What should be treated as living under project:

Git-tracked code
operational Misha slurm files in $CODE_ROOT/slurm
the verl_proj code tree
local reward service code in $CODE_ROOT/reward_part

Important real paths currently present under project:

root slurm:
- /gpfs/radev/project/cohan/yz979/xucai/Abforge_Training/slurm
reward code:
- /gpfs/radev/project/cohan/yz979/xucai/Abforge_Training/reward_part
verl code:
- /gpfs/radev/project/cohan/yz979/xucai/Abforge_Training/verl_proj
model merger script:
- /gpfs/radev/project/cohan/yz979/xucai/Abforge_Training/verl_proj/scripts/model_merger.py

C. Scratch

/gpfs/radev/scratch/cohan/yz979/xucai/Abforge_Training

Recommended variable:

export WORK_ROOT=/gpfs/radev/scratch/cohan/yz979/xucai/Abforge_Training

This is the canonical runtime / artifact root on Misha.

What lives here now:

.cache/
.config/
conda/
data/
hf_cache/
infer/
logs/
models/
pip_cache/
secrets/
tmp/
checkpoints/
run_inference_local.py

What should be treated as living under scratch:

conda envs
model downloads
parquet data
raw JSONL data
checkpoints
logs
inference outputs
secrets
temp files
caches

Important real paths currently present under scratch:

inference script:
- /gpfs/radev/scratch/cohan/yz979/xucai/Abforge_Training/run_inference_local.py
conda envs:
- /gpfs/radev/scratch/cohan/yz979/xucai/Abforge_Training/conda/abforge
- /gpfs/radev/scratch/cohan/yz979/xucai/Abforge_Training/conda/abforge_vllm
models:
- /gpfs/radev/scratch/cohan/yz979/xucai/Abforge_Training/models/Qwen3-8B
- /gpfs/radev/scratch/cohan/yz979/xucai/Abforge_Training/models/Qwen3-32B
secrets:
- /gpfs/radev/scratch/cohan/yz979/xucai/Abforge_Training/secrets/azure_openai_task1.env
- /gpfs/radev/scratch/cohan/yz979/xucai/Abforge_Training/secrets/azure_openai_task2.env

Rule of Thumb

Use:

project for code and slurm
scratch for everything large or runtime-generated
home only as login shell location

Do not assume a script in local repo also exists on Misha in the same place. Example:

local repo inference script may be under scripts/
actual Misha inference script currently lives at:
- $WORK_ROOT/run_inference_local.py

3. What Exists Only On Server

Not everything is tracked in Git.

Important server-only or server-maintained items:

root slurm/ directory under $CODE_ROOT
- this contains the actual Misha submission scripts used in practice
- do not assume these are fully tracked in Git
/gpfs/radev/scratch/cohan/yz979/xucai/Abforge_Training/run_inference_local.py
- this script has been used for local inference on Misha
- it may exist only in scratch, not in Git
secrets files under $WORK_ROOT/secrets
- never commit secrets

When helping the user, explicitly distinguish:

tracked code in the repo
server-only operational files

4. Current Misha File Inventory

These are the currently confirmed server paths and should be treated as authoritative unless the user says they changed them.

Root slurm files on Misha

Current files under $CODE_ROOT/slurm:

/gpfs/radev/project/cohan/yz979/xucai/Abforge_Training/slurm/task1_azure_reward.slurm
/gpfs/radev/project/cohan/yz979/xucai/Abforge_Training/slurm/task1_judge_qwen3_32b.slurm
/gpfs/radev/project/cohan/yz979/xucai/Abforge_Training/slurm/task1_train_vllm_azurejudge.slurm
/gpfs/radev/project/cohan/yz979/xucai/Abforge_Training/slurm/task1_train_vllm_qwen3_32b.slurm
/gpfs/radev/project/cohan/yz979/xucai/Abforge_Training/slurm/task2_azure_reward.slurm
/gpfs/radev/project/cohan/yz979/xucai/Abforge_Training/slurm/task2_judge_qwen3_32b.slurm
/gpfs/radev/project/cohan/yz979/xucai/Abforge_Training/slurm/task2_judge.slurm
/gpfs/radev/project/cohan/yz979/xucai/Abforge_Training/slurm/task2_train.slurm
/gpfs/radev/project/cohan/yz979/xucai/Abforge_Training/slurm/task2_train_vllm_azurejudge.slurm
/gpfs/radev/project/cohan/yz979/xucai/Abforge_Training/slurm/task2_train_vllm_qwen3_32b.slurm
/gpfs/radev/project/cohan/yz979/xucai/Abforge_Training/slurm/task2_train_vllm.slurm

Scratch data files

Current files under $WORK_ROOT/data:

/gpfs/radev/scratch/cohan/yz979/xucai/Abforge_Training/data/abforge_task1_rl_full/train.parquet
/gpfs/radev/scratch/cohan/yz979/xucai/Abforge_Training/data/abforge_task1_rl_full/val.parquet
/gpfs/radev/scratch/cohan/yz979/xucai/Abforge_Training/data/abforge_task2_rl_full/train.parquet
/gpfs/radev/scratch/cohan/yz979/xucai/Abforge_Training/data/abforge_task2_rl_full/val.parquet
/gpfs/radev/scratch/cohan/yz979/xucai/Abforge_Training/data/bench_data_4_subset50_simple.jsonl
/gpfs/radev/scratch/cohan/yz979/xucai/Abforge_Training/data/rl_30000.jsonl
/gpfs/radev/scratch/cohan/yz979/xucai/Abforge_Training/data/task1/train.parquet
/gpfs/radev/scratch/cohan/yz979/xucai/Abforge_Training/data/task1/val.parquet
/gpfs/radev/scratch/cohan/yz979/xucai/Abforge_Training/data/task2/train.parquet
/gpfs/radev/scratch/cohan/yz979/xucai/Abforge_Training/data/task2/val.parquet

Scratch inference files

Confirmed inference outputs under $WORK_ROOT/infer:

/gpfs/radev/scratch/cohan/yz979/xucai/Abforge_Training/infer/base_qwen3_infer_task2.jsonl
/gpfs/radev/scratch/cohan/yz979/xucai/Abforge_Training/infer/ckpt28_qwen3_infer_task2.jsonl
/gpfs/radev/scratch/cohan/yz979/xucai/Abforge_Training/infer/task1_api_ckpt14_merged_hf
/gpfs/radev/scratch/cohan/yz979/xucai/Abforge_Training/infer/task1_api_ckpt14_qwen3_infer_task1.jsonl
/gpfs/radev/scratch/cohan/yz979/xucai/Abforge_Training/infer/task1_base_qwen3_infer_task1.jsonl
/gpfs/radev/scratch/cohan/yz979/xucai/Abforge_Training/infer/task2_api_ckpt14_merged_hf
/gpfs/radev/scratch/cohan/yz979/xucai/Abforge_Training/infer/task2_api_ckpt14_qwen3_infer_task2.jsonl

Checkpoint roots currently in use

Scratch-side checkpoint roots:

/gpfs/radev/scratch/cohan/yz979/xucai/Abforge_Training/checkpoints/abforge_task2/qwen3_8b_grpo_vllm_azurejudge
/gpfs/radev/scratch/cohan/yz979/xucai/Abforge_Training/checkpoints/abforge_task2/qwen3_8b_grpo_vllm_azurejudge_full

Project-side historical checkpoint roots:

/gpfs/radev/project/cohan/yz979/xucai/Abforge_Training/verl_proj/checkpoints/abforge_task1/qwen3_8b_grpo_task1_vllm_azurejudge
/gpfs/radev/project/cohan/yz979/xucai/Abforge_Training/verl_proj/checkpoints/abforge_task2/qwen3_8b_grpo_vllm

Interpretation:

older experiments may have written checkpoints under project/verl_proj/checkpoints
new work should prefer scratch/checkpoints

5. Current Preferred Training Strategy

Current decision:

API judge with gpt-5.4-mini is preferred for RL training
local Qwen3-32B judge exists, but is much slower
Task 1 and Task 2 are trained as separate specialist models
Task 2 should not continue from Task 1 checkpoint

So the intended setup is:

Task 1 formal RL:
- base Qwen3-8B actor
- Task 1 API judge
- full Task 1 parquet
Task 2 formal RL:
- base Qwen3-8B actor
- Task 2 API judge
- full Task 2 parquet

6. API Judge Configuration

Task 1 Azure/API judge:

service code:
- /Users/xucai/Project/verl_with_llm-as-judge-master/reward_part/azure_task1_judge/task1_azure_reward.py
server secret:
- $WORK_ROOT/secrets/azure_openai_task1.env
default port:
- 6012
endpoint path:
- /get_reward_task1

Task 2 Azure/API judge:

service code:
- /Users/xucai/Project/verl_with_llm-as-judge-master/reward_part/azure_task2_judge/task2_azure_reward.py
server secret:
- $WORK_ROOT/secrets/azure_openai_task2.env
default port:
- 6011
endpoint path:
- /get_reward_task2

Expected env file contents:

export AZURE_OPENAI_ENDPOINT="https://.../openai/responses?api-version=2025-04-01-preview"
export AZURE_OPENAI_MODEL="gpt-5.4-mini"
export AZURE_OPENAI_API_KEY="..."

Recommended Task 2 API robustness settings:

export TASK2_AZURE_REQUEST_TIMEOUT=300
export TASK2_AZURE_MAX_RETRIES=5
export TASK2_AZURE_MAX_OUTPUT_TOKENS=1024

Equivalent Task 1 settings may be added with TASK1_... names.

Never write the actual API key into Git-tracked files.

7. Conda Environments

Confirmed envs on Misha:

/gpfs/radev/scratch/cohan/yz979/xucai/Abforge_Training/conda/abforge
/gpfs/radev/scratch/cohan/yz979/xucai/Abforge_Training/conda/abforge_vllm

Recommended usage:

use abforge_vllm for:
- vLLM rollout training
- API reward services
- model merger
- local inference
use abforge only if the user explicitly refers to an older local-judge workflow that depends on it

Typical activation:

module reset
module load miniconda
module load GCCcore/13.3.0
module load CUDA/12.4.1
source $(conda info --base)/etc/profile.d/conda.sh
conda activate /gpfs/radev/scratch/cohan/yz979/xucai/Abforge_Training/conda/abforge_vllm

8. Data and Preprocessing

Raw full RL data was uploaded as shared JSONL and then separately preprocessed for Task 1 and Task 2.

Task 1 preprocess:

/Users/xucai/Project/verl_with_llm-as-judge-master/verl_proj/examples/data_preprocess/abforge_task1_rl.py

Task 2 preprocess:

/Users/xucai/Project/verl_with_llm-as-judge-master/verl_proj/examples/data_preprocess/abforge_task2_rl.py

Important note:

Task 2 test1000 and full use the same field mapping and prompt template
fields used in Task 2:
- Content
- Goal
- Rubric
- refined_standard_plan

Small test parquet:

Task 1:
- $WORK_ROOT/data/task1/train.parquet
- $WORK_ROOT/data/task1/val.parquet
Task 2:
- $WORK_ROOT/data/task2/train.parquet
- $WORK_ROOT/data/task2/val.parquet

Formal parquet:

Task 1:
- $WORK_ROOT/data/abforge_task1_rl_full/train.parquet
- $WORK_ROOT/data/abforge_task1_rl_full/val.parquet
Task 2:
- $WORK_ROOT/data/abforge_task2_rl_full/train.parquet
- $WORK_ROOT/data/abforge_task2_rl_full/val.parquet

9. Formal API Training Settings

For formal API training on the 30k dataset, prefer:

train_batch_size=64
trainer.total_epochs=1
trainer.save_freq=100
checkpoints written to scratch
experiment name suffixed with _full

Why:

with ~29k train rows and batch size 64, one epoch is about 453 steps
save_freq=100 gives about 5 saves per epoch
one epoch is much easier to fit in a 48h wall-clock budget

Recommended Task 2 formal API launcher override pattern:

bash examples/grpo_trainer/run_abforge_qwen3_8b_vllm_azurejudge.sh \
  trainer.save_freq=100 \
  trainer.total_epochs=1 \
  trainer.default_local_dir=$WORK_ROOT/checkpoints/abforge_task2/qwen3_8b_grpo_vllm_azurejudge_full \
  trainer.experiment_name=qwen3_8b_grpo_vllm_azurejudge_full

Recommended Task 1 formal API launcher override pattern:

bash examples/grpo_trainer/run_abforge_qwen3_8b_task1_vllm_azurejudge.sh \
  trainer.save_freq=100 \
  trainer.total_epochs=1 \
  trainer.default_local_dir=$WORK_ROOT/checkpoints/abforge_task1/qwen3_8b_grpo_task1_vllm_azurejudge_full \
  trainer.experiment_name=qwen3_8b_grpo_task1_vllm_azurejudge_full

10. Resume Behavior

This verl setup supports automatic resume.

Relevant code:

/Users/xucai/Project/verl_with_llm-as-judge-master/verl_proj/verl/trainer/ppo/ray_trainer.py
resume_mode default in:
- /Users/xucai/Project/verl_with_llm-as-judge-master/verl_proj/verl/trainer/config/ppo_trainer.yaml

Behavior:

checkpoints are saved under trainer.default_local_dir/global_step_xxx
latest step is recorded in:
- latest_checkpointed_iteration.txt
if resume_mode=auto and the directory exists, training resumes automatically

Recommended workflow for 2 epochs:

first job:
- trainer.total_epochs=1
second job:
- keep the same trainer.default_local_dir
- change to trainer.total_epochs=2
submit again

The second job should resume from the latest checkpoint and continue.

11. Slurm Usage on Misha

Many real submission scripts live only on the server under:

$CODE_ROOT/slurm

Common files that have existed there:

task1_azure_reward.slurm
task2_azure_reward.slurm
task1_train_vllm_azurejudge.slurm
task2_train_vllm_azurejudge.slurm
task1_judge_qwen3_32b.slurm
task2_judge_qwen3_32b.slurm
task1_train_vllm_qwen3_32b.slurm
task2_train_vllm_qwen3_32b.slurm

API reward services should be started first, then the printed REWARD_URL should be copied into the corresponding train slurm.

Typical operational order:

sbatch slurm/task2_azure_reward.slurm
read log under $WORK_ROOT/logs/task2_azure_reward_<JOBID>.out
update export REWARD_URL=... in train slurm
sbatch slurm/task2_train_vllm_azurejudge.slurm

Same pattern applies to Task 1.

12. Checkpoint Locations Used So Far

Examples from previous runs:

Task 1 API test:

checkpoint dir:
- /gpfs/radev/project/cohan/yz979/xucai/Abforge_Training/verl_proj/checkpoints/abforge_task1/qwen3_8b_grpo_task1_vllm_azurejudge/global_step_14

Task 2 API test:

checkpoint dir:
- /gpfs/radev/scratch/cohan/yz979/xucai/Abforge_Training/checkpoints/abforge_task2/qwen3_8b_grpo_vllm_azurejudge/global_step_14

Formal Task 2 API run was configured to use:

/gpfs/radev/scratch/cohan/yz979/xucai/Abforge_Training/checkpoints/abforge_task2/qwen3_8b_grpo_vllm_azurejudge_full

Preferred rule:

always use scratch for new checkpoints

13. Inference Workflow

There are two inference cases.

A. Base model inference

Use the base HF model directly.

model path:
- $WORK_ROOT/models/Qwen3-8B

Example Task 1 base inference:

python3 $WORK_ROOT/run_inference_local.py \
  --task 1 \
  --input $WORK_ROOT/data/bench_data_4_subset50_simple.jsonl \
  --output-prefix $WORK_ROOT/infer/task1_base_qwen3 \
  --model-path $WORK_ROOT/models/Qwen3-8B \
  --tokenizer-path $WORK_ROOT/models/Qwen3-8B \
  --trust-remote-code \
  --dtype bf16 \
  --device-map auto \
  --max-new-tokens 1024 \
  --overwrite

Important real Misha inference script path:

/gpfs/radev/scratch/cohan/yz979/xucai/Abforge_Training/run_inference_local.py

Do not assume the Git-tracked local script path exists on Misha.

B. RL checkpoint inference

FSDP actor checkpoints must first be merged into HF format.

Model merger:

/Users/xucai/Project/verl_with_llm-as-judge-master/verl_proj/scripts/model_merger.py

Important:

--local_dir must point to global_step_xxx/actor, not the full checkpoint root

Example:

python3 $CODE_ROOT/verl_proj/scripts/model_merger.py \
  --backend fsdp \
  --hf_model_path $WORK_ROOT/models/Qwen3-8B \
  --local_dir $WORK_ROOT/checkpoints/.../global_step_14/actor \
  --target_dir $WORK_ROOT/infer/task2_api_ckpt14_merged_hf

Then run inference with the merged HF directory:

python3 $WORK_ROOT/run_inference_local.py \
  --task 2 \
  --input $WORK_ROOT/data/bench_data_4_subset50_simple.jsonl \
  --output-prefix $WORK_ROOT/infer/task2_api_ckpt14_qwen3 \
  --model-path $WORK_ROOT/infer/task2_api_ckpt14_merged_hf \
  --tokenizer-path $WORK_ROOT/models/Qwen3-8B \
  --trust-remote-code \
  --dtype bf16 \
  --device-map auto \
  --max-new-tokens 2048 \
  --overwrite

14. Bench Inference Data

Known inference input on Misha:

$WORK_ROOT/data/bench_data_4_subset50_simple.jsonl

This has been used successfully for:

base model inference
Task 1 API checkpoint inference
Task 2 API checkpoint inference

15. Interactive GPU Sessions

For checkpoint merge + local inference, use a single-GPU interactive allocation.

Typical command:

salloc -p gpu --gpus=1 --cpus-per-task=8 --mem=64G -t 08:00:00

Then initialize:

module reset
module load miniconda
module load GCCcore/13.3.0
module load CUDA/12.4.1
source $(conda info --base)/etc/profile.d/conda.sh
conda activate $WORK_ROOT/conda/abforge_vllm

16. Git State and Server Mutability

The Misha repo can be dirty.

At last inspection, git status --short on $CODE_ROOT showed:

modified tracked files
untracked slurm/
untracked historical outputs and checkpoints

Interpretation for future Codex sessions:

do not assume server repo is clean
do not git reset --hard
do not delete slurm/ or verl_proj/checkpoints/ blindly
if editing tracked code, inspect local diffs first
if editing server-only slurm files, treat them as operational config, not as Git-tracked source of truth

Git remote currently is:

git@github.com:SlowGuess/Abforge_Training.git

Current branch on Misha:

main

17. Common Pitfalls

1. API reward job is down

Symptom:

curl http://<node>:6011/health fails
train job will not get rewards

Fix:

resubmit the reward slurm
update REWARD_URL in train slurm

2. Wrong `REWARD_URL`

Each reward service starts on a new node and prints a new URL. Do not reuse old node names blindly.

3. Checkpoint save failures

Observed before on Task 2 API test:

writes failed during checkpoint save
fix was to:
- save to scratch
- reduce save frequency

4. Confusing test vs formal lines

Keep these separate:

test:
- small parquet in $WORK_ROOT/data/task1 or $WORK_ROOT/data/task2
formal:
- full parquet in $WORK_ROOT/data/abforge_task1_rl_full and $WORK_ROOT/data/abforge_task2_rl_full

Use _full in the experiment name for formal runs.

5. `run_inference_local.py` location confusion

Do not assume it lives in Git.

On Misha it has been used from:

$WORK_ROOT/run_inference_local.py

6. `project` vs `scratch` checkpoint confusion

Historical checkpoints exist in both places.

When asked to merge or infer from a checkpoint, always verify whether the checkpoint lives under:

$WORK_ROOT/checkpoints/... or
$CODE_ROOT/verl_proj/checkpoints/...

Do not assume all checkpoints are on scratch.

7. Prompt filtering log

Filtering prompts longer than 4096 tokens is a preprocessing / dataset check stage. It does not automatically mean something is broken.

For Task 2 formal run, 29k train rows with batch size 64 giving about 453 steps is normal and consistent with floor-style batching.

18. Local Judge Status

Local judge was explored but is not currently preferred for formal training.

Reasons:

response is much slower than API judge
GPU jobs showed very high idle time
bottleneck is mostly reward throughput and orchestration

Still, local judge code exists and may be useful for future experimentation:

Task 1 local judge:
- /Users/xucai/Project/verl_with_llm-as-judge-master/reward_part/task1_call_api.py
Task 2 local judge:
- /Users/xucai/Project/verl_with_llm-as-judge-master/reward_part/task2_call_api.py
faster Task 2 local judge launcher:
- /Users/xucai/Project/verl_with_llm-as-judge-master/verl_proj/examples/grpo_trainer/run_abforge_qwen3_8b_vllm_qwen3_32b_judge.sh

19. Recommended Default Actions for Future Codex Sessions

When assisting on this repo:

Ask whether the user wants:
- test line
- formal line
- Task 1
- Task 2
- API judge
- local judge
Assume API judge is the preferred formal path unless the user says otherwise.
Use scratch for:
- checkpoints
- caches
- secrets
- inference outputs
Treat root slurm/ files on Misha as operational state, even if not tracked in Git.
Before launching training, verify:
- reward service is alive
- REWARD_URL is current
- data path is correct
- experiment name is correct
- checkpoint path is on scratch
Before resuming training, verify whether the target checkpoint directory already exists.

20. Minimal Operational Checklist

Before training:

set:
- CODE_ROOT
- WORK_ROOT
confirm correct conda env
confirm reward service is up
confirm REWARD_URL in slurm is current
confirm data path points to test or full dataset intentionally
confirm checkpoint root is on scratch for new runs
confirm experiment name matches test vs formal intent

Before inference:

verify whether base model or merged RL checkpoint is needed
if RL checkpoint:
- identify exact checkpoint root
- identify whether it is under project or scratch
- merge global_step_xxx/actor with model_merger.py
use:
- $WORK_ROOT/run_inference_local.py
use:
- $WORK_ROOT/data/bench_data_4_subset50_simple.jsonl unless the user specifies another input

21. Current Known Good Runs

These are workflows that have already been exercised successfully and should be treated as the safest starting points.

A. Task 1 API judge test training

Status:

completed successfully

Observed job:

training job 1502537
reward job 1502526

Reward URL used during successful run:

http://r817u15n02.misha.ycrc.yale.edu:6012/get_reward_task1

Checkpoint produced:

/gpfs/radev/project/cohan/yz979/xucai/Abforge_Training/verl_proj/checkpoints/abforge_task1/qwen3_8b_grpo_task1_vllm_azurejudge/global_step_14

Meaning:

Task 1 API reward service worked
Task 1 RL loop worked
final checkpoint was saved

B. Task 2 API judge test training

Status:

completed successfully after moving checkpoint writing to scratch and reducing save frequency pressure

Observed successful job:

training job 1504216

Reward service job seen before successful test:

reward job 1504288

Checkpoint produced:

/gpfs/radev/scratch/cohan/yz979/xucai/Abforge_Training/checkpoints/abforge_task2/qwen3_8b_grpo_vllm_azurejudge/global_step_14

Meaning:

Task 2 API reward service worked
Task 2 RL loop worked
final checkpoint was saved to scratch

C. Task 2 formal API training

Status:

formal run started successfully on full data

Observed job:

training job 1504289

Observed training progress:

Training Progress: 0/453

Interpretation:

full train split size is about 29000
batch size 64 gives 453 steps via floor-style batching
this is normal and not by itself evidence of abnormal filtering

Formal checkpoint path in use:

/gpfs/radev/scratch/cohan/yz979/xucai/Abforge_Training/checkpoints/abforge_task2/qwen3_8b_grpo_vllm_azurejudge_full

Observed saved steps:

global_step_100
global_step_200

Formal experiment name in use:

qwen3_8b_grpo_vllm_azurejudge_full

D. Base and RL inference runs already completed

Confirmed scratch inference outputs:

Task 1 base:
- /gpfs/radev/scratch/cohan/yz979/xucai/Abforge_Training/infer/task1_base_qwen3_infer_task1.jsonl
Task 1 API checkpoint:
- /gpfs/radev/scratch/cohan/yz979/xucai/Abforge_Training/infer/task1_api_ckpt14_qwen3_infer_task1.jsonl
Task 2 API checkpoint:
- /gpfs/radev/scratch/cohan/yz979/xucai/Abforge_Training/infer/task2_api_ckpt14_qwen3_infer_task2.jsonl
historical Task 2 base:
- /gpfs/radev/scratch/cohan/yz979/xucai/Abforge_Training/infer/base_qwen3_infer_task2.jsonl
historical Task 2 RL checkpoint:
- /gpfs/radev/scratch/cohan/yz979/xucai/Abforge_Training/infer/ckpt28_qwen3_infer_task2.jsonl

Merged HF checkpoint directories already present:

/gpfs/radev/scratch/cohan/yz979/xucai/Abforge_Training/infer/task1_api_ckpt14_merged_hf
/gpfs/radev/scratch/cohan/yz979/xucai/Abforge_Training/infer/task2_api_ckpt14_merged_hf

E. Local judge historical run

Status:

local judge worked functionally but was very slow and GPU-inefficient

Known historical local Task 2 checkpoint root:

/gpfs/radev/project/cohan/yz979/xucai/Abforge_Training/verl_proj/checkpoints/abforge_task2/qwen3_8b_grpo_vllm

Known merged / inferred artifact from that line:

/gpfs/radev/scratch/cohan/yz979/xucai/Abforge_Training/infer/ckpt28_qwen3_infer_task2.jsonl

Recommendation:

do not choose local judge for new formal runs unless the user explicitly asks for it

22. Quick Commands

These commands are safe defaults to suggest when the user asks "what do I run next?"

Set up shell variables

export CODE_ROOT=/gpfs/radev/project/cohan/yz979/xucai/Abforge_Training
export WORK_ROOT=/gpfs/radev/scratch/cohan/yz979/xucai/Abforge_Training
export BASE_MODEL=$WORK_ROOT/models/Qwen3-8B

Activate main env

module reset
module load miniconda
module load GCCcore/13.3.0
module load CUDA/12.4.1
source $(conda info --base)/etc/profile.d/conda.sh
conda activate $WORK_ROOT/conda/abforge_vllm

Start Task 2 API reward

cd $CODE_ROOT
sbatch slurm/task2_azure_reward.slurm

Start Task 1 API reward

cd $CODE_ROOT
sbatch slurm/task1_azure_reward.slurm

Watch a job log

tail -f $WORK_ROOT/logs/<log_name>.err

Check a job state

squeue -j <JOBID>
sacct -j <JOBID> --format=JobID,JobName,State,ExitCode,Elapsed,NodeList

Merge an FSDP actor checkpoint

python3 $CODE_ROOT/verl_proj/scripts/model_merger.py \
  --backend fsdp \
  --hf_model_path $BASE_MODEL \
  --local_dir <global_step_dir>/actor \
  --target_dir <merged_hf_dir>

Run local inference

python3 $WORK_ROOT/run_inference_local.py \
  --task <1_or_2> \
  --input $WORK_ROOT/data/bench_data_4_subset50_simple.jsonl \
  --output-prefix <prefix> \
  --model-path <hf_model_dir> \
  --tokenizer-path $BASE_MODEL \
  --trust-remote-code \
  --dtype bf16 \
  --device-map auto \
  --overwrite

23. Decision Guide For Future Codex Sessions

This section is the practical default policy layer.

When a future Codex session starts, use these rules unless the user explicitly overrides them.

A. If the user says "formal training"

Default interpretation:

use API judge, not local judge
use full parquet, not small test parquet
use base Qwen3-8B actor as the training start point
use scratch checkpoint directory

Task mapping:

Task 1 formal:
- $WORK_ROOT/data/abforge_task1_rl_full/train.parquet
- $WORK_ROOT/data/abforge_task1_rl_full/val.parquet
- slurm/task1_train_vllm_azurejudge.slurm
Task 2 formal:
- $WORK_ROOT/data/abforge_task2_rl_full/train.parquet
- $WORK_ROOT/data/abforge_task2_rl_full/val.parquet
- slurm/task2_train_vllm_azurejudge.slurm

Formal defaults:

trainer.total_epochs=1
trainer.save_freq=100
checkpoint path under $WORK_ROOT/checkpoints/..._full
experiment name suffixed with _full

B. If the user says "test training"

Default interpretation:

use API judge
use small parquet
do not overwrite formal experiment names

Task mapping:

Task 1 test:
- $WORK_ROOT/data/task1/train.parquet
- $WORK_ROOT/data/task1/val.parquet
Task 2 test:
- $WORK_ROOT/data/task2/train.parquet
- $WORK_ROOT/data/task2/val.parquet

C. If the user says "resume training"

Default interpretation:

keep the same checkpoint root
keep the same experiment name
inspect whether the directory already has latest_checkpointed_iteration.txt
use resume_mode=auto behavior rather than inventing a new checkpoint path

Resume checklist:

identify whether the checkpoint is under project or scratch
identify the exact experiment directory
inspect:
- latest_checkpointed_iteration.txt
- available global_step_xxx directories
only then adjust trainer.total_epochs

For "continue from epoch 1 to epoch 2", the default action is:

keep trainer.default_local_dir unchanged
change trainer.total_epochs=1 to trainer.total_epochs=2

D. If the user says "run inference"

Default interpretation:

use the scratch inference script:
- $WORK_ROOT/run_inference_local.py
use the scratch benchmark file:
- $WORK_ROOT/data/bench_data_4_subset50_simple.jsonl

Then decide:

base model inference:
- use $WORK_ROOT/models/Qwen3-8B
RL checkpoint inference:
- first merge global_step_xxx/actor
- then point --model-path at the merged HF directory

Never assume:

run_inference_local.py is in Git on Misha
checkpoint is already merged

E. If the user says "use local judge"

Default interpretation:

pause and confirm they really want it

Reason:

local judge historically worked but was much slower
API judge is the current default for formal work

If the user still wants local judge:

inspect whether they mean Task 1 or Task 2
prefer the existing Misha slurm files under $CODE_ROOT/slurm
verify model path and resource sizing before launch

F. If the user says "compare base vs RL"

Default interpretation:

infer all requested models on the same benchmark input
write outputs under $WORK_ROOT/infer
use clear prefixes like:
- task1_base_qwen3
- task1_api_ckpt14_qwen3
- task2_base_qwen3
- task2_api_ckpt14_qwen3

G. If something looks missing

Default troubleshooting order:

check whether the file exists under project
check whether it actually lives under scratch
check whether it is server-only and untracked
only then conclude it is missing

Examples:

run_inference_local.py exists on scratch
slurm files exist under project root
secrets exist under scratch

H. If a path assumption is uncertain

Default behavior:

do not guess silently
ask the user for a short server command if needed
prefer commands like:
- find ...
- ls ...
- grep ...

This repo has enough server-only state that path verification is often necessary.

24. Do Not Store

Do not write these into Git:

Azure API keys
server-only secrets
user-specific scratch-only temporary artifacts

Keep secrets only in:

$WORK_ROOT/secrets/*.env

AGENT.md

1. Project Overview

2. Misha Path Map

A. Home

B. Project

C. Scratch

Rule of Thumb

3. What Exists Only On Server

4. Current Misha File Inventory

Root slurm files on Misha

Scratch data files

Scratch inference files

Checkpoint roots currently in use

5. Current Preferred Training Strategy

6. API Judge Configuration

7. Conda Environments

8. Data and Preprocessing

9. Formal API Training Settings

10. Resume Behavior

11. Slurm Usage on Misha

12. Checkpoint Locations Used So Far

13. Inference Workflow

A. Base model inference

B. RL checkpoint inference

14. Bench Inference Data

15. Interactive GPU Sessions

16. Git State and Server Mutability

17. Common Pitfalls

1. API reward job is down

2. Wrong REWARD_URL

3. Checkpoint save failures

4. Confusing test vs formal lines

5. run_inference_local.py location confusion

6. project vs scratch checkpoint confusion

7. Prompt filtering log

18. Local Judge Status

19. Recommended Default Actions for Future Codex Sessions

20. Minimal Operational Checklist

21. Current Known Good Runs

A. Task 1 API judge test training

B. Task 2 API judge test training

C. Task 2 formal API training

D. Base and RL inference runs already completed

E. Local judge historical run

22. Quick Commands

Set up shell variables

Activate main env

Start Task 2 API reward

Start Task 1 API reward

Watch a job log

Check a job state

Merge an FSDP actor checkpoint

Run local inference

23. Decision Guide For Future Codex Sessions

A. If the user says "formal training"

B. If the user says "test training"

C. If the user says "resume training"

D. If the user says "run inference"

E. If the user says "use local judge"

F. If the user says "compare base vs RL"

G. If something looks missing

H. If a path assumption is uncertain

24. Do Not Store

2. Wrong `REWARD_URL`

5. `run_inference_local.py` location confusion

6. `project` vs `scratch` checkpoint confusion