docs(c): prose signposts on the main() training loop

Full-sentence phase comments at the loop boundaries (the GRPO loop overview, the
per-prompt rollout/grade/accumulate phase). No logic moved; all 4 smoke arms'
training columns identical to baseline (cos diagnostics excluded; bf16 1e-3 noise).

Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>

src/projected_grpo/train.py

@@ -728,6 +728,10 @@ def main(cfg: Config) -> int:
     # that interactive bar sparse (tqdm's default maxinterval=10 forces 10s redraws).
     pbar = tqdm(range(steps), desc=f"train {cfg.arm} {cfg.preset_name}",
                 mininterval=120, maxinterval=120, disable=None)
+    # The GRPO training loop. Each step builds one batch of prompts, and for each
+    # prompt generates a mixed group (live student + cached teacher) rollouts, grades
+    # them, backpropagates the group-relative advantage, then projects the hack
+    # direction out of the gradient before the optimizer step.
     for step in pbar:
         t0 = time.time()
         opt.zero_grad(set_to_none=True)
@@ -842,6 +846,9 @@ def main(cfg: Config) -> int:
         # reward-subprocess-bound (-> parallel grading).
         t_gen = t_rew = t_fb = 0.0
 
+        # Generate and grade one prompt's rollout group at a time, accumulating its
+        # gradient into the shared knob (grad-accum keeps peak activation memory to a
+        # single group). The randint draw fixes which problem this slot trains on.
         for p_idx in range(prompts_per_step):
             idx = int(torch.randint(0, len(problems), (1,), generator=rng).item())
             prob = problems[idx]