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- Flatten teacher IPC data structure and use mb_idx*mbs indexing instead
  of cumulative mb_offset/mb_size for microbatch slicing
- Add log_softmax for teacher top-k logits in standard (non-IPC) path
- Restore output_replicated for teacher path and add kl_loss/nll_loss
  to aggregated results
- Make arrow config self-contained with explicit settings

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Refactor teacher logit sharing to use per-microbatch IPC buffers instead
of accumulating all teacher logits post-loop. Update loss functions to
handle optional microbatch indexing. Bump config to TP=4 and 10k steps.

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- Add `use_ipc` config flag to switch between IPC (in-process
  communication) and non-IPC (data-dict) teacher logprob paths
- Simplify KL loss to use (k+1)-dim distributions with a "rest"
  bucket, unifying IPC and non-IPC code paths
- Branch teacher inference and student training for both train
  and validation loops based on use_ipc setting
- Update submit script with IPC test experiment config

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- Add x_token/ module with TokenAligner for cross-vocabulary distillation
- Rewrite CrossTokenizerDistillationLossFn with chunk-averaged KL that
  handles 1:1, 1:many, many:1, and many:many token alignments, matching
  the original TokenAligner.compute_KL_loss_optimized() exactly
  (verified via sanity check: 0.00% difference)
- Fix teacher IPC to send full logits (topk_logits=None) instead of
  topk_logits=0 which produced empty tensors
- Pass global_top_indices through to projection for memory optimization
  (2.3GB -> 125MB for projection output tensor)
- Add cross-tokenizer data processing in training loop (dual tokenization,
  alignment, teacher data dict)
- Add unbuffered stdout for better SLURM log visibility
- Add example config and sanity check script

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Documents architecture, all new/modified files, usage instructions,
configuration reference, and design decisions for the cross-tokenizer
off-policy distillation feature built on NeMo RL v0.5.0.

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…compat

- Switch default teacher from Qwen3-8B to Phi-4-mini-instruct
- Add gold loss (common-vocab KL + uncommon-vocab L1) and xtoken loss modes
- Add CE loss with dynamic loss scaling option
- Replace dense projection with CSR sparse matmul for memory efficiency
- Add MMLU 5-shot evaluation benchmark
- Fix NotRequired import for Python <3.11 compatibility (16 files)
- Add submit_cross_tokenizer.sh sbatch script
- Add sanity check script for alignment and loss verification
- Update LR schedule for 80k step training (warmup 4k, cosine 76k)

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…parse projection

- Cache CrossTokenizerDistillationLossFn on policy workers at init and
  pass None to train() calls, eliminating repeated Ray serialization of
  the loss function (which includes large sparse matrices) each step.
- Add set_loss_fn() and update_cross_tokenizer_data() to Policy and
  DTensorPolicyWorkerV2 to support per-step cross-tokenizer data updates.
- Optimize sparse token projection by pre-reducing the sparse matrix
  with index_select before projection instead of projecting full vocab
  and slicing afterward.
- Use AutoConfig.from_pretrained() for vocab sizes in sanity check script.

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…P rank

Reduced training time with this optimization. Avoid Ray serialization of
the loss function by having each worker construct
CrossTokenizerDistillationLossFn from config + shared filesystem. Shard
teacher_input_ids and aligned_pairs per data-parallel rank instead of
broadcasting the full batch to every worker.

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- Add O(n+m) character-offset alignment via two-pointer walk on tokenizer
  offset mappings, with automatic DP fallback for failed samples
- Precompute canonical token ID maps at startup to skip convert_ids_to_tokens
- Add Numba JIT-accelerated DP kernel and banded DP variant
- Add KD preprocessor preserving raw text for teacher tokenization
- Add numba dependency
- Update config: expand arrow data glob, set max_num_epochs=1
- Update submit script: bump max_num_steps=10, rename experiment to raw-text-kd-16node

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Introduce CUDA kernel and Python integration module for faster TokenAligner dynamic programming base-case computation.

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… preprocessing with current-step GPU training while keeping alignment behavior unchanged.

Add explicit token-aligner runtime switches (`use_char_offset`, `use_align_fast`, CUDA-DP toggles), clean up dead/duplicated paths, and simplify the step orchestration with typed prefetch payloads and helper extraction for maintainability.

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Set explicit token aligner defaults and document the total-GPUs/2 heuristic for cross_tokenizer_num_workers so large-batch off-policy runs can iterate on stable, reproducible CT pool sizing.

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