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looped-elastic-depth-transformers Enable budget-conditioned reasoning by repeatedly applying a shared transformer block stack with trajectory-based conditioning on time and step size. Train via shortcut-consistency loss to align shorter and full-length trajectories, enabling variable-depth inference without retraining.
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Baseado na classificação ocupacional SOC
name looped-elastic-depth-transformers title LoopFormer: Elastic-Depth Looped Transformers for Latent Reasoning version 0.0.2 engine skillxiv-v0.0.2-claude-opus-4.6 license MIT url https://arxiv.org/abs/2602.11451 keywords ["Transformer Architecture","Elastic Depth","Looped Processing","Budget-Conditioned Reasoning","Self-Distillation"] description Enable budget-conditioned reasoning by repeatedly applying a shared transformer block stack with trajectory-based conditioning on time and step size. Train via shortcut-consistency loss to align shorter and full-length trajectories, enabling variable-depth inference without retraining.
LoopFormer: Elastic-Depth Looped Transformers for Latent Reasoning
Problem Context
Transformers with fixed depth have fixed computation. Extended reasoning requires more depth, but retraining is expensive. LoopFormer enables variable-depth inference by repeatedly applying the same transformer blocks, with trajectories conditioned on normalized time (0 to 1 over steps) and step size. Shorter trajectories remain informative while longer ones refine.
Core Concept
LoopFormer uses: (1) a shared stack of K transformer blocks applied repeatedly, (2) trajectory-based conditioning via time t ∈ [0,1] and step size Δt indicating position in loop, (3) shortcut-consistency training that aligns trajectories of different lengths, (4) AdaLN-style modulation of attention/FFN residual strengths.
Implementation
Step 1: Trajectory-based conditioning
import torch
import torch.nn as nn
import math
from typing import Tuple
class TrajectoryConditioner :
"""Encode time and step size for loop iteration."""
def __init__ (self, dim: int = 768 ):
self .dim = dim
def encode_trajectory_position (
self,
iteration_idx: int ,
total_iterations: int ,
step_size: float = None
) -> Tuple [torch.Tensor, torch.Tensor]:
"""
Encode normalized time t and step size Δt.
Args:
iteration_idx: Current loop iteration (0-based)
total_iterations: Total number of iterations
step_size: Size of current step (fraction of sequence)
Returns:
(time_embedding, step_embedding): [dim] tensors
"""
t = iteration_idx / max (1 , total_iterations - 1
if
is
None
1.0
self
self
return
def
_sinusoidal_encode
self, value: float
"""Sine-cosine encoding of scalar value."""
self
for
in
range
0
self
2
1
10000
self
if
self
if
1
self
1
return
def
condition_via_adaLN
self,
hidden_states: torch.Tensor,
time_embedding: torch.Tensor,
step_embedding: torch.Tensor
"""
Apply trajectory conditioning via learned modulation (AdaLN-style).
Learns: scale and bias that depend on time and step.
"""
1.0
0.1
0.05
return
Step 2: Looped transformer with shared blocks class LoopedTransformer (nn.Module):
"""Transformer with shared block stack applied repeatedly."""
def __init__ (
self,
dim: int = 768 ,
num_blocks: int = 6 ,
num_heads: int = 12 ,
max_loops: int = 16
):
super ().__init__()
self .dim = dim
self .num_blocks = num_blocks
self .max_loops = max_loops
self .transformer_blocks = nn.ModuleList([
self ._create_transformer_block(dim, num_heads)
for _ in range (num_blocks)
])
self .conditioner = TrajectoryConditioner(dim)
def _create_transformer_block (
self,
dim: int ,
num_heads: int
) -> nn.Module:
"""Create single transformer block with gating."""
class GatedTransformerBlock (nn.Module):
def __init__ (self, dim, num_heads ):
super ().__init__()
self .attn = nn.MultiheadAttention(dim, num_heads)
self .ffn = nn.Sequential(
nn.Linear(dim, 4 * dim),
nn.GELU(),
nn.Linear(4 * dim, dim)
)
self .attn_gate = nn.Parameter(torch.ones(1 ))
self .ffn_gate = nn.Parameter(torch.ones(1 ))
def forward (self, x ):
attn_out, _ = self .attn(x, x, x)
x = x + self .attn_gate * attn_out
ffn_out = self .ffn(x)
x = x + self .ffn_gate * ffn_out
return x
return GatedTransformerBlock(dim, num_heads)
def forward_looped (
self,
embeddings: torch.Tensor,
num_loops: int ,
return_intermediate: bool = False
) -> Tuple [torch.Tensor, list ]:
"""
Apply transformer blocks repeatedly in loops.
Args:
embeddings: Input embeddings
num_loops: Number of times to apply block stack
return_intermediate: Return all intermediate states
Returns:
(final_embeddings, intermediate_states)
"""
current_state = embeddings
intermediates = [current_state.clone()]
for loop_idx in range (num_loops):
time_emb, step_emb = self .conditioner.encode_trajectory_position(
loop_idx, num_loops
)
for block_idx, block in enumerate (self .transformer_blocks):
current_state = block(current_state)
current_state = self .conditioner.condition_via_adaLN(
current_state, time_emb, step_emb
)
if return_intermediate:
intermediates.append(current_state.clone())
return current_state, intermediates if return_intermediate else []
Step 3: Shortcut and consistency loss class ShortcutConsistencyTraining :
"""Train via multi-length trajectories with consistency."""
def __init__ (self, model: LoopedTransformer ):
self .model = model
def compute_consistency_loss (
self,
embeddings: torch.Tensor,
full_loops: int = 16 ,
shortcut_loops: int = 4
) -> torch.Tensor:
"""
Compute loss aligning short and full trajectories.
Strategy: Full trajectory as teacher, short trajectory as student.
"""
full_output, _ = self .model.forward_looped(embeddings, full_loops)
short_output, _ = self .model.forward_looped(embeddings, shortcut_loops)
consistency_loss = torch.nn.functional.mse_loss(short_output, full_output)
return consistency_loss
def compute_multi_trajectory_loss (
self,
embeddings: torch.Tensor,
target: torch.Tensor,
max_loops: int = 16 ,
sample_loops: list = None
) -> torch.Tensor:
"""
Train on multiple trajectory lengths simultaneously.
Args:
sample_loops: Which loop lengths to train on
(default: [4, 8, 12, 16])
"""
if sample_loops is None :
sample_loops = [4 , 8 , 12 , 16 ]
total_loss = torch.tensor(0.0 , requires_grad=True )
full_output, _ = self .model.forward_looped(embeddings, max_loops)
lm_loss = torch.nn.functional.cross_entropy(
full_output.view(-1 , full_output.size(-1 )),
target.view(-1 )
)
total_loss = total_loss + lm_loss
for num_loops in sample_loops:
if num_loops >= max_loops:
continue
short_output, _ = self .model.forward_looped(embeddings, num_loops)
shortcut_loss = torch.nn.functional.mse_loss(
short_output,
full_output.detach()
)
total_loss = total_loss + 0.1 * shortcut_loss
return total_loss
Step 4: Inference with elastic depth class ElasticDepthInference :
"""Perform inference with variable computational budget."""
def __init__ (self, model: LoopedTransformer ):
self .model = model
def generate_with_budget (
self,
prompt_embeddings: torch.Tensor,
budget_loops: int ,
schedule: str = 'linear'
) -> torch.Tensor:
"""
Generate with specified loop budget (variable depth).
Args:
budget_loops: Number of loops available
schedule: How to allocate loops ('linear', 'exponential')
Returns:
output: Generated embeddings
"""
if schedule == 'linear' :
num_loops = budget_loops
elif schedule == 'exponential' :
num_loops = min (budget_loops, int (math.sqrt(budget_loops)) * 4 )
else :
num_loops = budget_loops
output, _ = self .model.forward_looped(prompt_embeddings, num_loops)
return output
def benchmark_depth_vs_quality (
self,
test_embeddings: torch.Tensor,
target: torch.Tensor,
max_budget: int = 16
) -> dict :
"""Profile quality vs. computational budget."""
results = {}
for budget in range (1 , max_budget + 1 ):
output = self .generate_with_budget(test_embeddings, budget)
loss = torch.nn.functional.cross_entropy(
output.view(-1 , output.size(-1 )),
target.view(-1 )
)
results[budget] = {
'loss' : loss.item(),
'computation' : budget * len (self .model.transformer_blocks)
}
return results
Step 5: Full training loop def train_loopformer (
model: LoopedTransformer,
train_loader,
optimizer,
num_epochs: int = 10 ,
full_loops: int = 16 ,
shortcut_loops_list: list = None ,
device: str = 'cuda'
):
"""
Train LoopFormer with shortcut-consistency.
Args:
shortcut_loops_list: Which shortcut depths to train on
"""
if shortcut_loops_list is None :
shortcut_loops_list = [4 , 8 , 12 ]
consistency_trainer = ShortcutConsistencyTraining(model)
for epoch in range (num_epochs):
total_loss = 0.0
num_batches = 0
for batch_idx, (embeddings, target) in enumerate (train_loader):
embeddings = embeddings.to(device)
target = target.to(device)
loss = consistency_trainer.compute_multi_trajectory_loss(
embeddings, target,
max_loops=full_loops,
sample_loops=shortcut_loops_list
)
optimizer.zero_grad()
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0 )
optimizer.step()
total_loss += loss.item()
num_batches += 1
avg_loss = total_loss / num_batches
print (f"Epoch {epoch + 1 } : Loss={avg_loss:.4 f} " )
if (epoch + 1 ) % 5 == 0 :
print (" Depth vs Quality:" )
inference = ElasticDepthInference(model)
results = inference.benchmark_depth_vs_quality(
embeddings, target, max_budget=full_loops
)
for budget, metrics in results.items():
print (f" {budget} loops: loss={metrics['loss' ]:.4 f} " )
return model
Practical Guidance When to use : Variable-length reasoning; inference with heterogeneous compute budgets; amortized reasoning
num_blocks : 4-8 (shared blocks)max_loops : 12-20 (maximum depth)shortcut_loops : [4, 8, 12] (training depths)shortcut_loss_weight : 0.05-0.2 (balance main vs. consistency)num_heads : 8-16
Variable-depth inference without retraining
Shorter trajectories remain informative
Shared parameters reduce memory
Smooth quality scaling with loops
shortcut_loss too strong → longer trajectories don't improve
Max loops too large → diminishing returns, instability
Not validating intermediate trajectories actually work
Skip-consistency not tight enough
Scaling : Linear in num_loops; shared blocks reduce memory vs. fixed-depth stacks.
Reference Paper: https://arxiv.org/abs/2602.11451
Related work: Elastic networks, adaptive computation, trajectory-based conditioning
Benchmarks: Long reasoning sequences, language modeling
