Add GSM8K-L models and updated CEDR sampling presets

README.md

@@ -11,11 +11,11 @@ tags:
 - code
 ---
 
-# CEDR: Continuous Embedding Diffusion for Reasoning
+# CEDR: Continuous Embedding Diffusion Reasoner
 
-CEDR generates reasoning solutions with continuous latent diffusion and a learned prompt encoder. This repository contains six selected-weight inference packages for GSM8K, MATH500, and coding, before and after NFT.
+CEDR generates reasoning solutions with continuous latent diffusion and a learned prompt encoder.
 
-CEDR uses Qwen3-4B-Instruct-2507 as a frozen representation teacher during training and reuses its tokenizer and frozen token embeddings. CEDR’s diffusion backbone and prompt encoder are trained separately.
+CEDR uses Qwen3-4B-Instruct-2507 as a frozen representation teacher during training and reuses its tokenizer and frozen token embeddings. CEDR’s diffusion backbone and prompt encoder are trained separately. This repository contains eight selected-weight inference packages for GSM8K, MATH500, and coding, before and after NFT.
 
 Paper: [Reasoning with Continuous Latent Diffusion](https://arxiv.org/abs/2609.35694).
 
@@ -25,15 +25,21 @@ The training and inference code and installation instructions are at [chengxiang
 |---|---|---:|---:|
 | `gsm8k-b-pre-nft` | .9999 / .9999 | 64 | 2 |
 | `gsm8k-b-post-nft` | .99 / .99 | 64 | 2 |
+| `gsm8k-l-pre-nft` | .9999 / .9999 | 64 | 2 |
+| `gsm8k-l-post-nft` | .99 / .99 | 64 | 2 |
 | `math-l-pre-nft` | .9999 / .9999 | 64 | 3 |
 | `math-l-post-nft` | .99 / .99 | 64 | 3 |
 | `oci-l-pre-nft` | .9999 / .9999 | 128 | 3 |
-| `oci-l-post-nft` | .9 / .9 | 128 | 3 |
+| `oci-l-post-nft` | .99 / .99 | 128 | 3 |
 
-All use async clocks `(2.5, 2, 1.5)`, CFG 1, batch size 32, and a 1,024-token diffusion canvas. The GSM8K post-NFT preset caps decoded responses at 767 tokens. Other presets use the canvas available after each prompt.
+Defaults use DMD2-style re-noising with eta 1. ODE and EDM-style SDE (quantile-index factor 0.7) are selectable in the CLI. The supervised endpoints are GSM8K-B epoch 18, GSM8K-L/MATH-L epoch 21, and OCI-L epoch 14. NFT selections are updates 300, 600, 600, and 100, respectively.
+
+All use async clocks `(2.5, 2, 1.5)`, CFG 1, batch size 32, and a 1,024-token diffusion canvas. Both GSM8K post-NFT presets cap decoded responses at 767 tokens. Other presets use the canvas available after each prompt.
 
 ## Usage
 
+Use CEDR 0.2 or later for these sampler-aware packages (`cedr-inference-v2`).
+
 ```bash
 pip install 'git+https://github.com/chengxiang/CEDR.git#egg=cedr[data,math]'
 cedr download --model gsm8k-b-pre-nft --output artifacts/gsm8k-b-pre
@@ -48,13 +54,13 @@ See the code repository's evaluation guide for MATH500 and official HumanEval/MB
 
 Each package has FP32 learned denoiser/vocabulary-decoder weights and prompt-encoder weights in safetensors, architecture and inference configuration, selected-state metadata, and SHA-256 hashes. Keep the matched components together. The single shared BF16 token embedding table and tokenizer come from Qwen3-4B-Instruct-2507, revision `cdbee75f17c01a7cc42f958dc650907174af0554`. Generation requires no teacher Transformer or training cache. Each package also references a matched `representation.pt` for joint fine-tuning.
 
-The initial noise stream is CPU-seeded. Euler sampling uses a logit-normal quantile grid with mean -1.5 and standard deviation .8, local clock derivatives, and recurrent self-conditioning. Learned weights and ODE states remain FP32; denoiser Transformer forwards use BF16 and vocabulary projection uses FP32. Math/GSM prompt encoders run in FP32; coding prompt encoders run in BF16 with lengths padded to a multiple of 32.
+The initial noise stream is CPU-seeded. All samplers use a logit-normal quantile grid with mean -1.5 and standard deviation .8, and recurrent self-conditioning. ODE includes local clock derivatives and decodes its final Euler state. The stochastic samplers decode their final clean prediction and use a separate per-batch CUDA noise stream. See the [sampler guide](https://github.com/chengxiang/CEDR/blob/main/docs/samplers.md) for exact update, precision, and random-number conventions. Learned weights and sampler states remain FP32; denoiser Transformer forwards use BF16 and vocabulary projection uses FP32. Math/GSM prompt encoders run in FP32; coding prompt encoders run in BF16 with lengths padded to a multiple of 32.
 
 Generation supports resumption after completed batches. Downloads resolve an immutable Hub revision and verify every file. Use `--revision` to select a recorded release commit.
 
 ## Joint fine-tuning
 
-The three bundles under `representations/gsm8k-b`, `representations/math-l`, and `representations/oci-l` contain the exact FP32 centering means, effective whitened encoders, and reconstruction matrices. Each family shares its bundle across pre-NFT and post-NFT models. `cedr download` places the matched bundle in the model directory and verifies its hash.
+The four bundles under `representations/gsm8k-b`, `representations/gsm8k-l`, `representations/math-l`, and `representations/oci-l` contain the exact FP32 centering means, effective whitened encoders, and reconstruction matrices. Each family shares its bundle across pre-NFT and post-NFT models. `cedr download` places the matched bundle in the model directory and verifies its hash.
 
 Use `cedr train joint --init-weights MODEL_DIR` to initialize the downloaded ELF/prompt pair with fresh optimizers and independent EMAs. Download the pinned full Qwen teacher separately to encode your training data, using either live extraction or an optional feature cache. Separate covariance artifacts are unnecessary when keeping the representation fixed. See the [fine-tuning guide](https://github.com/chengxiang/CEDR/blob/main/docs/finetuning.md).
 
@@ -66,15 +72,17 @@ These are task-specific research models. Benchmark correctness is measured by th
 
 ## Release validation
 
-Seed-42 checks; separate from the paper's eight-seed estimates. All predictions and per-problem correctness decisions match.
+Seed-42 checks with package defaults; separate from the paper’s multi-seed estimates. All 7,360 predictions and per-problem correctness decisions match the reference evaluations.
 
 | Model | Benchmark | Correct / total | Pass@1 | Plus correct / total | Plus pass@1 |
 |---|---|---:|---:|---:|---:|
-| gsm8k-b-pre-nft | gsm8k | 523/1319 | 39.65% | — | — |
-| gsm8k-b-post-nft | gsm8k | 554/1319 | 42.00% | — | — |
-| math-l-pre-nft | math500 | 103/500 | 20.60% | — | — |
-| math-l-post-nft | math500 | 128/500 | 25.60% | — | — |
-| oci-l-pre-nft | humaneval | 49/164 | 29.88% | 46/164 | 28.05% |
-| oci-l-pre-nft | mbpp | 70/378 | 18.52% | 61/378 | 16.14% |
-| oci-l-post-nft | humaneval | 54/164 | 32.93% | 53/164 | 32.32% |
-| oci-l-post-nft | mbpp | 92/378 | 24.34% | 80/378 | 21.16% |
+| gsm8k-b-pre-nft | gsm8k | 637/1319 | 48.29% | — | — |
+| gsm8k-b-post-nft | gsm8k | 626/1319 | 47.46% | — | — |
+| gsm8k-l-pre-nft | gsm8k | 879/1319 | 66.64% | — | — |
+| gsm8k-l-post-nft | gsm8k | 898/1319 | 68.08% | — | — |
+| math-l-pre-nft | math500 | 145/500 | 29.00% | — | — |
+| math-l-post-nft | math500 | 158/500 | 31.60% | — | — |
+| oci-l-pre-nft | humaneval | 57/164 | 34.76% | 54/164 | 32.93% |
+| oci-l-pre-nft | mbpp | 123/378 | 32.54% | 101/378 | 26.72% |
+| oci-l-post-nft | humaneval | 70/164 | 42.68% | 66/164 | 40.24% |
+| oci-l-post-nft | mbpp | 110/378 | 29.10% | 88/378 | 23.28% |

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models/oci-l-post-nft/config.json

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models/oci-l-post-nft/manifest.json

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models/oci-l-pre-nft/manifest.json

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