Reinforcement-learning training checkpoint from the async-rl DAPO-Math
study, written by Trinity-RFT + verl. One repo holds exactly one
checkpoint; the step number is in the repo name and in this repo's marker
commit, titled Save model at global step 75.
Inside the study's own tooling (private repo yurun-yuan/async-rl) the whole
procedure is one command, which also restores the run-state files and writes the
tracker:
One thing to know if you restore by hand: the trainer discovers a checkpoint
only through latest_checkpointed_iteration.txt in the run directory, and
that file must be written last, after the weights are fully in place. Writing
it early is how a half-transferred checkpoint gets picked up.
The whole study
Every checkpoint from this study is archived the same way, one repo per step:
run
base model
steps archived
m4b_grpo_s40
Qwen3-4B
200, 225
m4b_grpoklr_s40
Qwen3-4B
275
m4b_klrneg_s40
Qwen3-4B
310
m4b_klr_pi_s40
Qwen3-4B
275 (plus step 280, weights only - see below)
m4b_reinforce_s40
Qwen3-4B
50, 75
m4b_klr_rollout_s40
Qwen3-4B
none
m17b_klr_pi_s1024
Qwen3-1.7B
4
m4b_klr_rollout_s40 has no checkpoints anywhere - this is expected, not a
gap in the archive. The trainer's own retention policy had already pruned every
step before the archive was made, so there was never anything to upload. Its
metrics and logs survive; its weights do not.
m4b_klr_pi_s40 step 280 exists only as
RyanYr/asyncrl-partial-m4b_klr_pi_s40-gs280:
the run died mid-save, so it has intact model weights but no optimizer state
and cannot be resumed from. It is deliberately outside the
asyncrl-<run>-gs<step> naming scheme so automated restores can never select it.
To continue that run, use step 275.
