Conveyor-Belt Diffusion LLM (64-Slot Dual-Channel Elastic Planning Oven)

A 28.88M-parameter hybrid Autoregressive + Diffusion Language Model where text generation works like a 64-slot bakery conveyor belt (inspired by Rolling Diffusion, FIFO-Diffusion, Diffusion Forcing, and AR-Diffusion):

• Input / Output Contract at every tick $t$: $$\text{Input: } \underbrace{[x_0, \dots, x_{t-1}]}{\text{Frozen Causal KV Prefix}} + \underbrace{[d_0^{(t)}, d_1^{(t)}, \dots, d{63}^{(t)}]}{\text{64-Slot Conveyor & Planning Belt}} \quad\longrightarrow\quad \text{Output: } \underbrace{x_t}{\text{Popped Slot-0 Token}} + \underbrace{[d_0^{(t+1)}, \dots, d_{63}^{(t+1)}]}_{\text{Shifted & Refined Belt}}$$

• Live Demo & Pretrained Weights:

  • Hugging Face Live Space: https://huggingface.co/spaces/sumitk/diffusion-conveyer-llm
  • Pretrained 24h Checkpoint (best.pt): https://huggingface.co/sumitk/diffusion-conveyer-llm

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Key Architectural Innovations

1. Dual-Channel "Soft-Vocab + Gated-Thought" Dough (model.py):
   • Channel 1 (Lexical Anchor): Expected vocabulary embedding $e_{\text{soft}, k} = p_k^\top E_{\text{vocab}}$ under a positional temperature schedule $\tau_k \in [0.5, 4.0]$ (soft and exploratory at the back of the belt, crisp at the front).
   • Channel 2 (Continuous Thought Highway): Gated residual thought state $m_k \in \mathbb{R}^d$ initialized with forget-gate bias $+1.0$, fused via $\alpha_k = k / 63$ so $\alpha_0 = 0$ exactly at Slot 0 (guaranteeing zero distribution mismatch when $x_t$ commits into the causal KV cache).
2. Two-Zone Human-Style Thinking & Speaking Belt:
   • Front 25% Articulation Stream (Slots 0..15): Shifts left by 1 slot every tick ($s_k = 1.0$), polishing immediate grammar toward Slot 0.
   • Mid-Belt Clause Expansion (Slot 15): Synthesizes new intermediate phrase embeddings (expand_head) at the boundary between the paused planning zone and moving front conveyor.
   • Back 75% Elastic Planning Horizon (Slots 16..63): Spans a $2\times$ wider lookahead horizon with a learned shift/pause gate $s_k \in (0, 1)$, holding sentence-level goals while clauses form in the middle.
3. Self-Healing Belt Recovery Training (losses.py):
   • Trained with 16-step unrolled BPTT, full-sequence causal LM supervision (prefix_lm_weight=1.0), front-belt shift-consistency KL, DAgger-style scheduled sampling, and mid-unroll belt corruption so the belt actively repairs noisy or contradictory plans.

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24-Hour Training Results on 2,000,000 Real TinyStories

Trained for 24.00 hours (122,743 steps) on a single RTX 5070 Ti GPU across 2,000,000 real TinyStories (0% synthetic templates, 4,096 compact tiktoken BPE vocabulary with byte fallback for zero <UNK>):

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Quickstart

1. Install

pip install -e .

2. Launch the Interactive 64-Slot Conveyor Web Simulator

PYTHONPATH=src python -m conveyer.web_sim --port 8766

(If runs/tinystories/best.pt is not present locally, it will automatically download the pretrained 28.88M checkpoint from huggingface.co/sumitk/diffusion-conveyer-llm.)

3. CLI Tick-by-Tick Generation & Belt Inspection

PYTHONPATH=src python -m conveyer.infer \
  --ckpt runs/tinystories/best.pt \
  --prompt "Once upon a time, Lily and a little bunny walked into the green forest to find the golden key near the locked treasure chest. Suddenly a big lion" \
  --tokens 80 --overlap 10

4. Run the Full Benchmark Suite

PYTHONPATH=src python -m conveyer.eval --ckpt runs/tinystories/best.pt

5. Train / Resume 24-Hour Real TinyStories Run

PYTHONPATH=src python -u -m conveyer.train --config configs/tinystories.yaml --resume auto