methods: sdft + sdpo — self-distillation trainers for SFT and RL

[ghanshyam-lyzr]

Two new methods on both local backends (mlx + torch), bringing the count to 15. Both make the model its own teacher: instead of cross-entropy on golden tokens or a scalar reward per rollout, the student's on-policy samples are pushed toward the same model reading richer context.

• sdft (arXiv 2601.19897): on-policy learning from demonstrations. Each chat row's golden response is placed in-context for the teacher pass; the student's sampled completion is distilled toward the demonstration-conditioned distributions. Learns the task like SFT with far less catastrophic forgetting. Knobs: sdft_alpha (forward KL ↔ reverse KL ↔ JSD), completion length, temperature, teacher template.

• sdpo (arXiv 2601.20802): RL via self-distillation on the grpo data surface. Rollout groups are scored by reward_fns — a fn may return (score, feedback) pairs — then each rollout is distilled toward the self-teacher reading a successful sibling solution and/or the textual feedback. Dense per-token advantages; all-fail groups still teach. The teacher trails the student as an EMA over adapter weights.

Shared prompt-building and group-scoring helpers live in shadowlm/sdft.py and shadowlm/sdpo.py (pure stdlib) so both backends and the CPU tests use one implementation. Ships CPU tests, GPU matrix cells, mlx/torch/remote examples, and Train-page params (grouped under Preference · RL · distillation; no held-out eval, like grpo). The remote backend's reward_fns error now covers sdpo.

pyproject: pin mlx-lm<0.30 and mlx-lm-lora==1.0.0 — newer releases require transformers 5.x / mlx-lm 0.30.6+, past our transformers<4.57 ceiling. Frontend static bundle rebuilt.