Control policy entropy dynamics in RL by reweighting gradients from clipped tokens. CE-GPPO preserves out-of-clip gradients with beta parameters to stabilize exploration-exploitation balance, preventing entropy collapse while maintaining training stability in LLM fine-tuning.
Control policy entropy dynamics in RL by reweighting gradients from clipped tokens. CE-GPPO preserves out-of-clip gradients with beta parameters to stabilize exploration-exploitation balance, preventing entropy collapse while maintaining training stability in LLM fine-tuning.
Outcome: Stable Entropy Control in Policy Optimization
CE-GPPO enables fine-grained control over policy entropy dynamics during reinforcement learning training, preventing entropy collapse while maintaining training stability. This is critical for LLM fine-tuning where premature convergence to deterministic policies undermines exploration and model reasoning capabilities.
Problem Context
Standard policy gradient methods like PPO use gradient clipping to constrain policy updates, but this creates a hidden cost: gradients from tokens outside the clipping interval (low-probability and high-probability tokens) are discarded. This asymmetric gradient loss accelerates entropy collapse.
In LLM training, entropy collapse manifests as the model converging too quickly to high-probability tokens, losing exploration capability needed for complex reasoning tasks. Analysis shows that:
Positive Advantage, Low Probability (PA&LP) tokens: Their clipped gradients would encourage exploration but are discarded
Negative Advantage, Low Probability (NA&LP) tokens: Their clipped gradients accelerate convergence but dominate the update
The covariance between log-probabilities and advantages drives entropy change. When clipped gradients are dropped, this covariance increases unnaturally, forcing rapid policy convergence.
Core Concept
CE-GPPO reframes entropy control as a gradient reweighting problem. Instead of discarding clipped-token gradients, the method incorporates them with tunable weights beta1 and beta2:
beta1: Controls weight on NA&LP gradients (negative advantage, low probability)
beta2: Controls weight on PA&LP gradients (positive advantage, low probability)
The key insight: policy entropy change is governed by the covariance between log-probabilities and advantages. By reweighting these specific gradient sources, CE-GPPO directly controls entropy dynamics without separate entropy regularization terms. This preserves gradient information while maintaining the pessimistic clipping behavior of standard PPO, ensuring stable optimization.
Architecture Overview
Gradient Classification: Tokens are classified into three groups based on importance sampling ratio and clipping bounds
Weighted Gradient Preservation: Out-of-clip gradients are multiplied by importance ratios, then weighted by beta parameters
Stable Integration: Non-clipped tokens follow standard PPO gradients; clipped tokens contribute weighted gradients proportional to their importance
Hyperparameter Control: Beta values directly tune entropy dynamics without requiring separate loss terms or complex tuning
The method maintains PPO's pessimistic update mechanism: when advantages are positive but ratios are too high, updates are clipped; when advantages are negative but ratios are too low, updates are clipped. CE-GPPO adds controlled gradient flow from these clipped regions.
Implementation Overview
Step 1: Token Classification and Ratio Computation
For each token in a batch, compute the importance sampling ratio (new policy probability / old policy probability) and classify it relative to clipping bounds [1-epsilon, 1+epsilon].
# Token-level importance sampling and classificationimport torch
import torch.nn.functional as F
defcompute_token_importance_ratios(
log_probs_new: torch.Tensor, # [batch, seq_len]
log_probs_old: torch.Tensor, # [batch, seq_len]
advantages: torch.Tensor, # [batch, seq_len]
epsilon: float = 0.2) -> tuple:
"""
Compute importance ratios and token classifications.
Returns:
ratios: importance sampling ratios
is_in_clip: boolean mask for tokens inside clipping interval
is_pa_lp: boolean mask for PA&LP tokens (pos advantage, clipped low)
is_na_lp: boolean mask for NA&LP tokens (neg advantage, clipped low)
"""# Importance sampling: exp(log_new - log_old)
ratios = torch.exp(log_probs_new - log_probs_old)
# Clipping bounds
lower_bound = 1.0 - epsilon
upper_bound = 1.0 + epsilon
# Determine which tokens are inside the clipping interval
is_in_clip = (ratios >= lower_bound) & (ratios <= upper_bound)
# PA&LP: positive advantage but ratio too low (below lower bound)
is_pa_lp = (advantages > 0) & (ratios < lower_bound)
# NA&LP: negative advantage but ratio too high (above upper bound)
is_na_lp = (advantages < 0) & (ratios > upper_bound)
return ratios, is_in_clip, is_pa_lp, is_na_lp
Step 2: Compute CE-GPPO Objective Loss
The CE-GPPO loss combines standard clipped losses with weighted out-of-clip gradients.
defce_gppo_loss(
log_probs_new: torch.Tensor,
log_probs_old: torch.Tensor,
advantages: torch.Tensor,
epsilon: float = 0.2,
beta_1: float = 0.5,
beta_2: float = 1.0) -> torch.Tensor:
"""
CE-GPPO loss with gradient-preserving clipping.
Args:
log_probs_new: log probabilities under new policy [batch, seq_len]
log_probs_old: log probabilities under old policy [batch, seq_len]
advantages: advantage estimates [batch, seq_len]
epsilon: PPO clipping parameter
beta_1: weight for NA&LP token gradients
beta_2: weight for PA&LP token gradients
Returns:
loss: scalar CE-GPPO loss (negate for gradient ascent)
"""
ratios, is_in_clip, is_pa_lp, is_na_lp = compute_token_importance_ratios(
log_probs_new, log_probs_old, advantages, epsilon
)
# Initialize loss accumulator
loss = torch.zeros(1, device=log_probs_new.device, dtype=log_probs_new.dtype)
# Standard PPO clipped loss for tokens inside interval
clipped_ratio = torch.clamp(ratios, 1.0 - epsilon, 1.0 + epsilon)
standard_loss = -torch.min(
ratios * advantages,
clipped_ratio * advantages
)
loss = loss + standard_loss[is_in_clip].mean()
# Weighted gradients for PA&LP tokens (encourage exploration)# Stop gradient on clipped ratio to preserve original probability gradientsif is_pa_lp.any():
pa_lp_loss = -beta_2 * (ratios * advantages)
loss = loss + pa_lp_loss[is_pa_lp].mean()
# Weighted gradients for NA&LP tokens (accelerate convergence)if is_na_lp.any():
na_lp_loss = -beta_1 * (ratios * advantages)
loss = loss + na_lp_loss[is_na_lp].mean()
return loss / (is_in_clip.sum().float() + is_pa_lp.sum().float() + is_na_lp.sum().float()).clamp(min=1.0)
Step 3: Batch-Level Training Loop Integration
Integrate CE-GPPO into a standard RL training loop with value function updates.
defce_gppo_train_step(
model: torch.nn.Module,
batch_log_probs_new: torch.Tensor,
batch_log_probs_old: torch.Tensor,
batch_advantages: torch.Tensor,
batch_values_new: torch.Tensor,
batch_returns: torch.Tensor,
optimizer: torch.optim.Optimizer,
epsilon: float = 0.2,
beta_1: float = 0.5,
beta_2: float = 1.0,
value_coeff: float = 0.5,
entropy_coeff: float = 0.01) -> dict:
"""
Single CE-GPPO training step combining policy and value updates.
Args:
model: policy and value network
batch_log_probs_new: new policy log probs [num_samples, seq_len]
batch_log_probs_old: old policy log probs [num_samples, seq_len]
batch_advantages: advantage estimates [num_samples, seq_len]
batch_values_new: value predictions [num_samples]
batch_returns: cumulative returns [num_samples]
optimizer: torch optimizer
epsilon: PPO clipping parameter
beta_1: weight for NA&LP gradients
beta_2: weight for PA&LP gradients
value_coeff: coefficient for value loss
entropy_coeff: coefficient for entropy regularization
Returns:
metrics: dict with loss components
"""
optimizer.zero_grad()
# Compute policy loss
policy_loss = ce_gppo_loss(
batch_log_probs_new,
batch_log_probs_old,
batch_advantages,
epsilon,
beta_1,
beta_2
)
# Compute value loss (standard MSE)
value_loss = F.mse_loss(batch_values_new, batch_returns)
# Optional entropy regularization (usually disabled with CE-GPPO)# Entropy from the new policy
log_probs_dist = torch.exp(batch_log_probs_new)
entropy = -(log_probs_dist * batch_log_probs_new).sum(dim=-1).mean()
# Combined loss
total_loss = policy_loss + value_coeff * value_loss - entropy_coeff * entropy
# Backward pass and optimization
total_loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)
optimizer.step()
return {
"policy_loss": policy_loss.item(),
"value_loss": value_loss.item(),
"entropy": entropy.item(),
"total_loss": total_loss.item()
}
Step 4: LLM-Specific Integration with Transformers
Adapt CE-GPPO for transformer-based LLMs, handling token sequences and attention masks.
defcompute_ce_gppo_gradients_for_lm(
model: torch.nn.Module,
input_ids: torch.Tensor, # [batch, seq_len]
attention_mask: torch.Tensor, # [batch, seq_len]
advantage_scores: torch.Tensor, # [batch, seq_len-1]
old_logits: torch.Tensor, # [batch, seq_len-1, vocab_size]
epsilon: float = 0.2,
beta_1: float = 0.5,
beta_2: float = 1.0) -> torch.Tensor:
"""
Compute CE-GPPO loss for language model fine-tuning.
Args:
model: transformer language model
input_ids: token indices
attention_mask: padding mask
advantage_scores: token-level advantages from reward model
old_logits: cached logits from old policy
epsilon, beta_1, beta_2: CE-GPPO hyperparameters
Returns:
loss: scalar loss for backpropagation (negate for gradient ascent)
"""# Forward pass to get new logits
outputs = model(input_ids, attention_mask=attention_mask, output_hidden_states=False)
new_logits = outputs.logits[:, :-1, :] # [batch, seq_len-1, vocab_size]# Get target token indices (next tokens in sequence)
target_tokens = input_ids[:, 1:] # [batch, seq_len-1]# Compute log probabilities
log_probs_new = F.log_softmax(new_logits, dim=-1)
log_probs_old = F.log_softmax(old_logits, dim=-1)
# Extract log probabilities for target tokens
log_probs_new = torch.gather(log_probs_new, -1, target_tokens.unsqueeze(-1)).squeeze(-1)
log_probs_old = torch.gather(log_probs_old, -1, target_tokens.unsqueeze(-1)).squeeze(-1)
# Mask out padding tokens
advantage_scores = advantage_scores * attention_mask[:, 1:]
# Compute CE-GPPO loss
loss = ce_gppo_loss(
log_probs_new,
log_probs_old,
advantage_scores,
epsilon=epsilon,
beta_1=beta_1,
beta_2=beta_2
)
return loss
Practical Guidance
Hyperparameter Configuration
Parameter
Default
Range
Impact
beta_1
0.5
[0.0, 1.0]
Weight for NA&LP (negative advantage, low probability) gradients. Higher values accelerate convergence but may increase entropy decay rate.
beta_2
1.0
[0.0, 1.0]
Weight for PA&LP (positive advantage, low probability) gradients. Higher values encourage exploration by preserving encouraging-but-clipped gradients.
epsilon
0.2
[0.15, 0.3]
PPO clipping range. Larger epsilon increases clipping region, affecting how many gradients are reweighted.
entropy_coeff
0.0
[0.0, 0.01]
Coefficient for entropy regularization. CE-GPPO typically sets this to 0 since entropy is controlled via betas.
value_coeff
0.5
[0.1, 1.0]
Coefficient for value function loss in combined objective.
When to Use CE-GPPO
CE-GPPO is recommended for:
LLM fine-tuning with RL: When training language models with GRPO, DPO variants, or custom reward signals
Entropy collapse symptoms: When observing rapid policy convergence and degraded downstream performance in mid-training
Fine-grained entropy control: When you need to tune exploration-exploitation balance without separate entropy regularization
Training stability: When other entropy mitigation methods (plain entropy regularization) cause gradient instability
Example scenarios: training models on AIME, mathematical problem-solving, reasoning-heavy instruction following.
When NOT to Use CE-GPPO
Do not use CE-GPPO when:
Simple supervised fine-tuning: If your task doesn't require sustained exploration (e.g., basic instruction-following from a strong teacher policy), standard PPO or simpler methods are sufficient
Already converged policies needed: If your goal is rapid convergence to the best policy and exploration is explicitly undesired
Reward signal is noisy: CE-GPPO amplifies gradients from edge cases (clipped tokens). Noisy rewards will create unstable training signals in these regions
Very small model sizes: Overhead of tracking multiple gradient components may not justify benefits for tiny models
Single-turn generation: Tasks without complex sequential decision-making don't benefit from entropy control
Start with defaults (beta_1=0.5, beta_2=1.0): These settings balance entropy preservation with convergence
Monitor entropy curves: Track policy entropy throughout training. CE-GPPO should show stable, gradually declining entropy
Adjust beta_1 if entropy decays too slowly: Increase beta_1 to accelerate convergence (weight NA&LP gradients more)
Adjust beta_2 if entropy decays too fast: Increase beta_2 to preserve exploration (weight PA&LP gradients more)
Validate on held-out benchmarks: Ensure entropy dynamics correlate with improved downstream performance
Test robustness: CE-GPPO shows robustness to moderate hyperparameter changes; small adjustments (±0.25) typically don't degrade performance
Common Pitfalls
Setting both betas to 0: Degenerates to standard PPO with entropy collapse. At least one beta should be non-zero
Excessive entropy regularization alongside CE-GPPO: Double-counting entropy control leads to divergence. Set entropy_coeff=0 when using CE-GPPO
Ignoring base policy stability: CE-GPPO assumes the old policy (policy_old in ratios) is stable. If old policy changes rapidly between updates, reweighting becomes unstable
Over-weighting PA&LP gradients: Very high beta_2 (>1.5) can cause gradient explosion in early training. Keep beta_2 ≤ 1.0
Misaligned advantage signals: If advantages are incorrectly computed or scaled, clipped-token reweighting will amplify errors. Validate advantage computation independently
Training Dynamics Insights
Early training: High entropy is generally beneficial. Maintain stable, high entropy to allow broad exploration
Mid training: Entropy should gradually decline as the policy discovers good solutions. CE-GPPO controls the rate
Late training: Entropy stabilizes at a non-zero level. This prevents mode-collapse and allows sampling of diverse solutions
Research shows that CE-GPPO achieves the best balance when maintaining "relatively high and stable entropy" throughout training, with greater weight on PA&LP gradients (higher beta_2) than NA&LP gradients (lower beta_1).
Reference
Paper: CE-GPPO: Controlling Entropy via Gradient-Preserving Clipping Policy Optimization in Reinforcement Learning
Authors: Zhenpeng Su, Leiyu Pan, Minxuan Lv, Yuntao Li, Wenping Hu, Fuzheng Zhang, Kun Gai, Guorui Zhou
@article{su2025cegppo,
title={CE-GPPO: Controlling Entropy via Gradient-Preserving Clipping Policy Optimization in Reinforcement Learning},
author={Su, Zhenpeng and Pan, Leiyu and Lv, Minxuan and Li, Yuntao and Hu, Wenping and Zhang, Fuzheng and Gai, Kun and Zhou, Guorui},
journal={arXiv preprint arXiv:2509.20712},
year={2025}
}
Key Experimental Results
On mathematical reasoning benchmarks (AIME24, AIME25, HMMT25, MATH500, AMC23):
1.5B model: 45.2% → 54.9% average benchmark accuracy (+9.7 points)
7B model: 60.8% → 67.5% average benchmark accuracy (+6.7 points)
Compared to:
GRPO baseline: Suffers from entropy collapse
DAPO: Overly high entropy in early training, slower performance gains
CE-GPPO: Stable entropy curves with consistent performance improvements across benchmarks
Implementation tested on DeepSeek-R1-Distill models with mathematical reasoning datasets (30k samples). Gradients remain stable with KL divergence and gradient norms within expected ranges relative to standard PPO.
