The Silent Weight Mismatch: Debugging a Zero-Acceptance EAGLE-3 Drafter

In the high-stakes world of speculative decoding for large language models, a drafter that predicts zero tokens correctly is worse than useless — it actively degrades performance. When the assistant in this opencode session launched a newly trained EAGLE-3 draft model against the Kimi-K2.5 target model on SGLang, the acceptance metrics told a brutal story: accept_len: 1.00, accept_rate: 0.20. With five draft tokens configured, an acceptance rate of 0.20 means exactly one token is accepted — the base token from the verification pass. Every single draft token was being rejected. The model that had achieved 74.5% step-0 accuracy during validation was producing what amounted to random noise at inference time.

Message 3540 is the turning point — the moment the assistant identifies the first of two critical bugs responsible for this catastrophic failure. It is a masterclass in diagnostic reasoning, combining source code analysis, checkpoint inspection, and an understanding of how two independently developed software systems (the speculators training library and the SGLang inference engine) encode fundamentally different assumptions about weight naming conventions.

The Frustrating Zero-Acceptance Mystery

The path to message 3540 was paved with mounting frustration. The assistant had spent the previous rounds (messages 3514–3536) launching and relaunching the SGLang server, waiting through interminable startup sequences, and running benchmarks that consistently returned ~25 tok/s — far below the 90 tok/s baseline achieved without speculation. The server logs repeatedly showed the same pattern: accept len: 1.00, accept rate: 0.20. This was identical to the behavior of the old vLLM-trained drafter, which had been dismissed as a hidden-state mismatch issue. The new drafter, trained on 10,000 samples of SGLang-extracted hidden states, was supposed to fix that. Yet it exhibited the exact same failure mode.

The assistant's initial hypothesis, stated in message 3537, was that this was "NOT a hidden-state-mismatch issue" but rather "a fundamental issue — likely a weight key mismatch or missing weight transformation when SGLang loads our draft model checkpoint." This was a crucial reframing. Instead of blaming the data (the hidden states used for training), the assistant correctly shifted focus to the inference pipeline — how the trained weights were being loaded and interpreted by SGLang.

The Moment of Insight

Message 3540 opens with the assistant reading the load_weights method of SGLang's LlamaForCausalLMEagle3 class. The reasoning is laid out in three crisp bullet points:

1. SGLang's model has self.model.midlayer (a single decoder layer) 2. Our checkpoint saves weights as layers.0.* (from speculators convention) 3. The load_weights method tries to map layers.0.* → model.layers.0.*

The insight is elegant: the speculators library — the training framework used to fine-tune the EAGLE-3 draft model — saves the single decoder layer under the key prefix layers.0.. This is a natural convention for transformer models, which typically have multiple layers indexed numerically. But SGLang's EAGLE-3 implementation, which only uses a single decoder layer for the draft head, names this layer midlayer rather than layers.0. The load_weights method, written to handle the general case, attempts to map checkpoint keys to model parameters by trying the raw key name first, then prepending model.. So layers.0.hidden_norm.weight becomes model.layers.0.hidden_norm.weight — which doesn't exist. The actual parameter is model.midlayer.hidden_norm.weight. The weights are silently dropped.

This is a particularly insidious bug because it produces no error message. The weight loading code simply skips keys that don't match any parameter, leaving the decoder layer with its random initialization. The model loads successfully, runs inference, and produces plausible-looking output — but the draft predictions are essentially random, leading to zero acceptance.

The Verification Step

The assistant immediately moves to verify the hypothesis by inspecting the checkpoint keys directly. The command in message 3540 uses Python's safetensors library to open the saved model file and print all keys:

=== Our checkpoint keys ===
  d2t
  embed_tokens.weight
  fc.weight
  layers.0.hidden_norm.weight
  layers.0.input_layernorm.weight
  layers.0.mlp.down_proj.weight
  layers.0.mlp.gate_proj.weight
  layers.0.mlp.up_proj.weight
  layers.0.post_attention_layernorm.weight
  layers.0.self_attn.k_proj.weight
  layers.0.self_attn.o_proj.weight
  layers.0.self_attn.q_proj.weight
  layers.0.self_attn.v_proj.weight
  lm_head.weight
  norm.weight
  t2d

Every parameter from the decoder layer is prefixed with layers.0.. Meanwhile, the earlier inspection of SGLang's model (message 3541) had revealed that the corresponding parameters live under midlayer.*. The mismatch is confirmed.

Input Knowledge Required

To fully appreciate this message, one needs several layers of context. First, an understanding of the EAGLE-3 speculative decoding architecture: the draft model is a lightweight "drafter" that predicts multiple future tokens in parallel, using hidden states from the target model as conditioning. The drafter has its own embedding layer, a fusion layer (fc) that projects concatenated hidden states, a single transformer decoder layer (midlayer), and an output projection (lm_head).

Second, familiarity with the speculators library's training conventions: when the assistant trained the drafter using the speculators framework, it saved weights using the HuggingFace/transformers convention of layers.N.* for decoder layers. This is the standard format for models like LLaMA, where multiple layers are indexed from 0.

Third, knowledge of SGLang's model implementation: the LlamaForCausalLMEagle3 class defines a single decoder layer named midlayer rather than layers.0. This is an intentional design choice — since there's only one layer, the name midlayer is more descriptive. But it creates an incompatibility with the speculators checkpoint format.

Fourth, an understanding of how load_weights works in SGLang: the method iterates over checkpoint keys and tries to match them to model parameters by checking the raw key name and then model.{key}. It does not attempt any remapping or fallback for naming differences. Keys that don't match are silently skipped.

The Deeper Problem

While the weight key mismatch is the immediate discovery of message 3540, the chunk summary reveals that this is only half the story. The second, more fundamental issue is that the hidden states passed to the draft model are 7168-dimensional (single layer) rather than the expected 21504-dimensional concatenation of three auxiliary layer hidden states. The fc fusion layer, which projects 21504 → 7168, is never applied because the shape check hidden_states.shape[-1] != embeds.shape[-1] evaluates to 7168 != 7168 — False — bypassing the fusion entirely.

This explains why both the old vLLM-trained drafter and the new SGLang-trained drafter exhibit identical zero-acceptance behavior: they both receive single-layer hidden states at inference time despite being trained on fused multi-layer features. The root cause is that eagle_use_aux_hidden_state is not properly activated for the KimiK25 model, or the target model's capture_aux_hidden_states mechanism is not producing the multi-layer hidden states.

Output Knowledge Created

Message 3540 produces several valuable outputs. First, it establishes a clear diagnosis of the weight key mismatch as a contributing factor to the zero-acceptance problem. Second, it provides a concrete verification methodology — inspecting checkpoint keys and comparing them to model parameter names — that can be applied to similar debugging scenarios. Third, it generates a specific fix: renaming layers.0.* to midlayer.* in the checkpoint file. This fix is implemented in the subsequent messages (3542–3544) with a Python script that performs the key remapping.

Assumptions and Potential Mistakes

The assistant's reasoning in message 3540 rests on several assumptions. The primary assumption is that the load_weights method silently drops unmatched keys rather than raising an error. This turns out to be correct for SGLang's implementation, but it's worth noting that this behavior is itself a design flaw — silent failures in weight loading are dangerous because they produce no diagnostic signal.

Another assumption is that the weight key mismatch is the cause of the zero-acceptance problem. While it's certainly a contributing factor, the later discovery about auxiliary hidden state dimensions reveals a deeper issue. Even after fixing the key names, the acceptance rate barely improves (from 0.20 to 0.21), confirming that the weight mismatch alone wasn't the full story. The assistant's framing in message 3537 — "This is NOT a hidden-state-mismatch issue" — turns out to be partially incorrect. The hidden state dimensionality mismatch is a critical factor, just one that manifests at inference time rather than during training.

The Thinking Process

What makes message 3540 particularly compelling is the visible reasoning process. The assistant doesn't just state the conclusion — it walks through the logical steps: examining SGLang's model structure, comparing it to the checkpoint format, tracing through the load_weights method, and identifying the exact point of failure. The three-bullet-point structure at the top of the message is a model of clear technical communication: problem, cause, mechanism.

The decision to verify with a concrete command — actually printing the checkpoint keys — demonstrates the principle of "trust but verify." Rather than relying solely on code analysis, the assistant confirms the hypothesis by inspecting real data. This combination of source code reading and empirical validation is the hallmark of effective debugging.

In the broader arc of the session, message 3540 represents a pivot from data-centric debugging (wondering whether the training data was sufficient) to pipeline-centric debugging (examining how weights flow from training to inference). This reframing ultimately leads to the discovery of not one but two critical bugs, and sets the stage for the deeper investigation into SGLang's auxiliary hidden state mechanism that follows in subsequent messages.