[faresobeid]

What

Drop recipe lines that only repeat what the config already resolves to. The resolved configs do not change, except for skip_optimizer (see below).

• [weight_broadcast] type = "nccl": rl already picks NCCL when inference is managed and LoRA is off. Removed from minimax-m2.5 swe, intellect-3.1 rl, and qwen3-30b-a3b swe/math. The block stays in examples/extra/dynamo/rl.toml (no managed inference, so the default there is filesystem) and in glm-5.3 (it also sets port and timeout).
• ac.freq = 1 (the default) is removed from 8 recipes. The [*.model.ac] table header stays, matching the GLM-4.5-Air recipes.
• trainer.dist_timeout_seconds = 3600 / dist_timeout_seconds = 3600 (the default) is removed from 5 RL recipes and vlm/sft-moe.
• inference.vllm.enable_return_routed_experts = true is removed where trainer.enable_router_replay = true already sets it (glm-4.5-air ×4, nemotron-3-super).
• [trainer.ckpt] skip_optimizer = true is removed from glm-4.5-air swe-budget/search/terminal and nemotron-3-super swe.
  • It only affects loading: on resume, it drops the AdamW/Muon state.
  • It came from the exp: scaleswe ablation #2862/exp: search + terminal ablations #2915 ablation configs. None of these recipes warm-starts from another run's checkpoint. They train from the HF model and resume their own checkpoints, which include optimizer state.
  • configs/ci/integration/reverse-text-sft/full-offload-resume.toml keeps it on purpose: it tests that path.
• docs/scaling.md: the fused_lm_head_token_chunk_size default is 8192 (since Increase chunk size to 8k #3302), not 1024.

Not touched

• fused_lm_head_token_chunk_size = 1024 pins (7 recipes) and attn = "flash_attention_3" pins (10). Should they stay as memory/hardware choices?
• [ac_offloading] max_inflight_activations lines (a separate PR removes the field).
• The [weight_broadcast] blocks of the GLM-4.5-Air and nemotron recipes (handled in the weight_broadcast.timeout PR, to avoid a conflict).

Test plan

• pytest tests/unit/orchestrator tests/unit/test_configs.py -m "not gpu": 334 passed (validates every TOML under configs/ and examples/).
• Resolved configs of all 14 edited recipes, main vs this branch (RLConfig/SFTConfig model_dump):
  • identical, except trainer.ckpt.skip_optimizer True → False in the 4 recipes above;
  • the checkout path in template_path also differs (main checkout vs this worktree), which is not a recipe change.

---

Note

Low Risk
Mostly documentation and deduplication; the only resolved-config change is skip_optimizer false on four recipes that resume with full optimizer state, which matches their intended training flow.

Overview
Trims RL/SFT example and advanced recipe TOMLs by deleting settings that already match framework defaults, so recipes only spell out non-default choices.

Removed redundant [weight_broadcast] type = "nccl", trainer.model.ac freq = 1, dist_timeout_seconds = 3600, and inference.vllm.enable_return_routed_experts where NCCL broadcast, AC frequency, dist timeout, and routed-expert return are implied (e.g. managed inference + enable_router_replay). Dropped [trainer.ckpt] skip_optimizer = true from four GLM-4.5-Air and Nemotron SWE/search/terminal recipes so resume loads optimizer state from their own checkpoints instead of discarding it.

docs/scaling.md now documents fused_lm_head_token_chunk_size default 8192 (was incorrectly 1024). Explicit pins like fused_lm_head_token_chunk_size = 1024 in memory-tight recipes are unchanged.

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[mikasenghaas]

ah ya nice