Add DPO data generation & training; fix checkpoint labels; switch to WSD scheduler; update optimizer ablations in abstract/conclusion

main.tex

@@ -18,7 +18,7 @@
 \DeclareMathOperator{\Var}{Var}
 
 \title{End-to-End Training of a 1.2B Transformer with AstrAI \\
-       \large Data Pipeline, Distributed Training, and BF16 Numerical Stability via Residual Scaling}
+       \large Data Pipeline, Distributed Training, and Ablations on Optimizer, Initialization, and BF16 Numerical Stability}
 
 \author{AstrAI Contributors}
 \date{}
@@ -34,21 +34,24 @@ JSON-driven BBPE preprocessing with multi-strategy packing, HDF5/mmap
 storage backends, and a companion SFT pipeline ({\sc Alembic}) with MinHash
 deduplication and LLM-as-Judge scoring. The 24-layer decoder uses GQA,
 SwiGLU, RoPE, and RMSNorm, trained with a hybrid Muon/AdamW optimizer and
-cosine scheduling under DDP/FSDP. A BF16 stability analysis shows that
+WSD (Warmup--Stable--Decay) scheduling under DDP/FSDP. A BF16 stability analysis shows that
 GPT-2 residual scaling substantially reduces per-block residual variance
 accumulation, keeping post-training variance well below the overflow
 threshold of standard initialization; empirically this yields a sustained
 loss advantage over Kaiming initialization throughout training.
-Post-training weight distribution analysis across three
-checkpoints---varying optimizer, initialization, and training
-budget---confirms that residual-scaled projections maintain narrow
-distributions throughout training, preserving the numerical stability
-established at initialization. An SVD-based effective rank analysis
-further reveals that the model operates near its representational
-capacity, with attention Q/O projections consistently showing lower
-utilization than K/V projections, a pattern stable across all
-configurations and consistent with the low-rank structure induced by
-grouped query attention.
+Systematic ablations show that the hybrid Muon/AdamW optimizer
+outperforms pure AdamW on 2D weight matrices, and that GPT-2 residual
+scaling yields a sustained loss advantage over both Kaiming and Normal
+initialization throughout training. Post-training weight distribution
+analysis across three checkpoints---varying optimizer, initialization,
+and training budget---confirms that residual-scaled projections
+maintain narrow distributions throughout training, preserving the
+numerical stability established at initialization. An SVD-based
+effective rank analysis further reveals that the model operates near
+its representational capacity, with attention Q/O projections
+consistently showing lower utilization than K/V projections, a pattern
+stable across all configurations and consistent with the low-rank
+structure induced by grouped query attention.
 \end{abstract}
 
 % ======================================================================
@@ -60,8 +63,10 @@ model architecture. Data must be preprocessed and stored efficiently, the
 training loop must handle distributed parallelism, gradient accumulation,
 checkpointing, and logging---and numerical pitfalls must be diagnosed and
 fixed. This paper describes the complete workflow using {\sc AstrAI}~\cite{astrai}, an
-open-source framework for Transformer training and inference, and highlights a
-BF16 precision issue encountered along the way.
+open-source framework for Transformer training and inference. Beyond the
+pipeline, we conduct systematic ablations on optimizers (hybrid Muon/AdamW
+versus pure AdamW) and initializations (GPT-2 residual scaling, Kaiming,
+and Normal), and analyse a BF16 precision issue encountered along the way.
 
 % ======================================================================
 \section{Data Pipeline}
@@ -140,10 +145,33 @@ pipeline proceeds as follows:
 An optional LLM-as-Judge scoring module provides multi-dimensional
 quality scores that can be used to filter low-quality samples.
 
-An IFD (Instruction Fulfillment Difficulty) analysis is provided in
-Appendix~\ref{app:ifd}.
+\subsection{DPO Data Generation}
 
-% ======================================================================
+To construct pairwise preference data for Direct Preference
+Optimization, we start from a bilingual (Chinese--English) instruction
+set curated by the same MinHash pipeline described above.  For each
+prompt $x$ in the instruction set, we generate two responses:
+
+\begin{itemize}[nosep]
+  \item \textbf{Chosen} $y_w$: generated by the reference model
+    $\pi_{\text{ref}}$, i.e.~the SFT checkpoint after supervised
+    fine-tuning on the curated instruction data.
+  \item \textbf{Rejected} $y_l$: generated by the base model
+    $\pi_{\text{base}}$, i.e.~the model at the end of pretraining
+    before any instruction tuning.
+\end{itemize}
+
+Both generations use greedy decoding to eliminate sampling variance and
+ensure that the preference signal reflects model capability rather than
+decoding randomness.  The resulting preference pairs
+$(x, y_w, y_l)$ are stored in the same JSONL format used for SFT and
+fed directly into the DPO training loop
+(Section~\ref{sec:dpo}).  Because the base model and the reference
+model share the same architecture but differ in instruction-following
+ability, the contrast between $y_w$ and $y_l$ is sharp and consistent,
+which stabilises the DPO gradient updates.  All instructions are
+balanced across Chinese and English domains to preserve bilingual
+alignment capability during preference optimisation.
 \section{Model Architecture}
 % ======================================================================
 
@@ -231,8 +259,8 @@ The model is trained on next-token cross-entropy loss:
 \mathcal{L} = -\sum_{t=1}^{T} \log P(x_t \mid x_{<t}; \theta).
 \end{equation}
 
-Training uses a hybrid optimizer: Muon for 2D weight matrices and AdamW~\cite{loshchilov2019adamw} for 1D parameters (embeddings, biases, LayerNorm), with cosine learning rate
-scheduling (2\% warmup) and global L2 gradient clipping. The framework supports DDP and FSDP for multi-GPU distribution,
+Training uses a hybrid optimizer: Muon for 2D weight matrices and AdamW~\cite{loshchilov2019adamw} for 1D parameters (embeddings, biases, LayerNorm), with WSD (Warmup--Stable--Decay)
+learning rate scheduling (2\% warmup) and global L2 gradient clipping. The framework supports DDP and FSDP for multi-GPU distribution,
 with gradient accumulation to manage memory.
 Table~\ref{tab:train_params} lists the key hyperparameters.
 
@@ -245,14 +273,16 @@ Table~\ref{tab:train_params} lists the key hyperparameters.
 \textbf{Hyperparameter} & \textbf{Value} \\
 \midrule
 Precision & BF16 (weights + optimizer states) \\
-Optimizer & AdamW, $\eta=1.5\times10^{-4}$ \\
+  Optimizer & Hybrid Muon/AdamW$^a$, $\eta=1.5\times10^{-4}$ \\
 Betas & $(0.9, 0.95)$, weight decay $0.1$ \\
 Gradient clip & Global L2, max norm $1.0$ \\
-Scheduler & Cosine, warmup ratio $0.02$ \\
+  Scheduler & WSD (warmup 2\%, stable, decay) \\
 Batch size & 4 per device $\times$ 4 GPUs $\times$ 32 accumulation \\
 Sequence length & 2,048 tokens \\
-Total steps & 19,000 \\
+  Total tokens & $\sim$25B ($\approx$23k steps) \\
 \bottomrule
+\multicolumn{2}{@{}l@{}}{\footnotesize $^a$Muon applied to 2D weight matrices (attention projections and FFN layers);}\\
+\multicolumn{2}{@{}l@{}}{\footnotesize \phantom{$^a$}AdamW applied to 1D parameters (embeddings, biases, LayerNorm scales).}\\
 \end{tabular}
 \end{table}
 
@@ -280,8 +310,8 @@ Figure~\ref{fig:ckpt_comparison} compares four configurations. The left panel sh
 rate schedule for the hybrid Muon/AdamW configuration across $\sim$25B
 tokens. The Muon optimizer is applied to all 2D weight matrices
 (attention projections and FFN layers), while AdamW handles 1D
-parameters (embeddings, biases, and normalization scales). The cosine
-schedule with 2\% warmup is visible in the lower panel.}
+parameters (embeddings, biases, and normalization scales). The WSD
+schedule (warmup, stable phase, and final decay) is visible in the lower panel.}
 \label{fig:muon_pt}
 \end{figure}
 
@@ -289,9 +319,9 @@ Figure~\ref{fig:muon_pt} shows the extended training dynamics of the
 Muon optimizer configuration over $\sim$25B tokens. The loss curve
 exhibits the expected power-law decay in the early phase (0--5B tokens),
 followed by a gradual plateau as the model approaches convergence on
-the pretraining distribution. The cosine learning rate schedule
-reaches its minimum at the end of training, ensuring stable weight
-updates in the final phase.
+the pretraining distribution. The WSD schedule holds the learning rate
+constant during the long stable phase and then decays at the end of
+training, ensuring stable weight updates in the final phase.
 
 \begin{figure}[H]
 \centering
@@ -300,7 +330,7 @@ updates in the final phase.
 mixed Chinese--English instruction dataset: training loss, learning
 rate, and gradient norm. The loss drops rapidly in the first 200
 steps and then enters a slower decay phase. The learning rate
-follows a cosine schedule with 2\% warmup. Gradient norms stabilize
+follows a WSD schedule (2\% warmup, stable, decay). Gradient norms stabilize
 after approximately 300 steps, indicating that the fine-tuning
 process has reached a stable optimization regime.}
 \label{fig:sft_metrics}
@@ -331,7 +361,14 @@ preferred response $y_w$ and dispreferred response $y_l$, the loss is:
 \end{equation}
 where $\beta$ controls the KL-divergence penalty against the reference.
 We use $\beta=0.1$, a batch of 64 preference pairs per step, and the
-same hybrid Muon/AdamW optimizer with cosine scheduling.
+same hybrid Muon/AdamW optimizer.  Unlike the pretraining and SFT
+phases, which both employ WSD (warmup--stable--decay) scheduling to
+maintain a long high-learning-rate plateau, DPO alignment uses a
+cosine schedule with short linear warmup.  The shorter ~1{,}500-step
+alignment run does not benefit from an extended stable phase; instead,
+cosine decay lowers the learning rate steadily, which discourages
+over-optimisation away from the reference distribution and matches the
+observed convergence pattern in Figure~\ref{fig:dpo_metrics}.
 
 \begin{figure}[H]
 \centering
@@ -459,7 +496,7 @@ identified in the theoretical analysis (Section~\ref{sec:num-stability}).
 To verify that the residual-scaling constraint persists throughout
 training---not just at initialization---we compare weight value
 distributions across three checkpoints:
-\texttt{kami-15bt} (Muon, 15B tokens), \texttt{norm-15bt} (Normal
+\texttt{kami-15bt} (AdamW, GPT-2 residual scaling, 15B tokens), \texttt{norm-15bt} (AdamW, Normal
 init, 15B tokens), and \texttt{muon-25bt} (Muon, 25B tokens).
 
 Figure~\ref{fig:ckpt_weight_density} shows the kernel density
@@ -476,13 +513,16 @@ are visible in the per-component weight std
         ($\sim$0.024 for attention projections), exceeding both 15B
         checkpoints ($\sim$0.015--0.021), reflecting continued weight
         drift from $\sigma_0 = 0.02$ as training progresses.
-  \item \textbf{Muon constrains early-stage drift}: at equal training
-        budget (15B tokens), the Muon-trained \texttt{kami-15bt} shows
+  \item \textbf{Residual scaling constrains early-stage drift}: at
+        equal training budget (15B tokens) and with the same AdamW
+        optimizer, the GPT-2-scaled \texttt{kami-15bt} shows
         \emph{smaller} std ($\sim$0.015) than the Normal-init
-        \texttt{norm-15bt} ($\sim$0.021). Muon's orthogonalization
-        step normalizes the update direction, constraining per-step
-        weight movement. At 25B tokens, cumulative steps overtake
-        this per-step constraint, producing the largest overall std.
+        \texttt{norm-15bt} ($\sim$0.021), indicating that residual
+        scaling itself limits weight drift beyond its initialization
+        effect. The Muon-trained \texttt{muon-25bt} (25B tokens)
+        exhibits the largest std because cumulative training steps
+        eventually overtake both initialization and per-step optimizer
+        constraints.
 \end{itemize}
 
 Critically, the residual-scaled projections remain bounded at
@@ -522,8 +562,17 @@ by a factor of 48, keeping post-24-layer variance at $1.34$ versus $17.5$
 without scaling. Post-training weight distribution analysis confirms that
 this scaling constraint persists throughout training, with residual-scaled
 projections maintaining narrow distributions ($\sigma \approx 0.003$)
-regardless of optimizer or training duration. An SVD-based effective rank
-analysis (Appendix~\ref{app:eff_rank}) further reveals that the model
+regardless of optimizer or training duration.
+
+Optimizer ablations (Figure~\ref{fig:ckpt_comparison}) demonstrate that
+the hybrid Muon/AdamW configuration consistently outperforms pure AdamW
+with both Kaiming and Normal initializations, achieving lower training loss
+and faster convergence. Within the Muon family, applying AdamW to the
+embedding layer rather than Muon yields a further loss reduction and more
+stable gradient norms, confirming that 1D parameters benefit from
+second-moment adaptation while 2D weight matrices gain from Muon's
+orthogonalisation step. An SVD-based effective rank analysis
+(Appendix~\ref{app:eff_rank}) further reveals that the model
 operates near its representational capacity. The complete framework and
 model weights are available at \url{https://github.com/ViperEkura/AstrAI}.
 
@@ -787,7 +836,7 @@ remain bounded.}
 \begin{tabular}{@{}lccc@{}}
 \toprule
 \textbf{Component} & \textbf{kami-15bt} & \textbf{norm-15bt} & \textbf{muon-25bt} \\
- & (Muon, 15B) & (Normal, 15B) & (Muon, 25B) \\
+ & (AdamW, 15B) & (AdamW, 15B) & (Muon, 25B) \\
 \midrule
 attn.q\_proj          & 0.0154 & 0.0207 & 0.0230 \\
 attn.k\_proj          & 0.0153 & 0.0206 & 0.0238 \\