Are complicated loss functions necessary for teaching LLMs to reason?

GRPO's PPO-style clipping and policy ratio constraints are unnecessary for teaching LLMs to reason; a simplified REINFORCE variant with group relative advantage estimation (RGRA) achieves competitive or superior performance on mathematical benchmarks.

Problem Statement

GRPO has become a popular post-training method for improving LLM reasoning, but its complexity—combining clipping, KL regularization, and group relative advantage—makes it opaque and computationally involved. It is unclear which components are actually responsible for performance gains, making it hard to iterate, debug, or simplify. This paper addresses the lack of ablation understanding in RL-based LLM training pipelines.

Key Novelty

Systematic ablation study of GRPO identifying that negative feedback (penalizing below-baseline actions) is essential, while PPO-style clipping is not
Proposal of RGRA (REINFORCE with Group Relative Advantage), a stripped-down algorithm removing policy ratio clipping and PPO constraints while retaining group relative advantage estimation
Empirical evidence that simpler REINFORCE-based RL methods can match or outperform GRPO on standard math reasoning benchmarks

Evaluation Highlights

RGRA achieves stronger or comparable performance to GRPO across standard mathematical reasoning benchmarks (e.g., GSM8K, MATH), suggesting PPO-style constraints are not the source of gains
Ablations show that training solely on positive actions (above baseline) severely limits learning, confirming negative feedback is a critical design choice

Breakthrough Assessment

5/10 This is a solid, practically useful contribution that simplifies the RL training recipe for LLM reasoning and provides clear ablation insights, but it is primarily an empirical simplification study rather than a fundamentally new method or paradigm.

Methodology

Systematically ablate each component of GRPO (clipping, KL regularization, negative feedback, group relative advantage) to isolate their individual contributions to mathematical reasoning performance
Identify that negative feedback and group relative advantage are the key drivers of performance, while PPO-style clipping adds complexity without consistent benefit
Propose and evaluate RGRA—a REINFORCE algorithm using group relative advantage estimation without policy ratio clipping—on standard math benchmarks against GRPO baselines

System Components

Group Relative Advantage Estimation

Computes advantage for each action relative to the mean reward of a group of sampled actions, providing a stable and informative training signal without a learned value function

Negative Feedback

Penalizes actions that fall below the group baseline reward, which the paper identifies as essential for effective learning—training only on positive signals is insufficient

PPO-style Clipping (Removed in RGRA)

The policy ratio clipping and trust-region constraints from GRPO that are shown to be unnecessary and are stripped out in the proposed RGRA method

RGRA (REINFORCE with Group Relative Advantage)

The proposed simplified algorithm combining standard REINFORCE policy gradient updates with group relative advantage, omitting PPO constraints for a more transparent and efficient training procedure

Results

Benchmark	GRPO	RGRA	Delta
Math Reasoning (aggregate)	Baseline	Comparable or better	Positive or neutral
Negative feedback ablation	Full GRPO	Positive-only training degrades significantly	Large negative when removed
PPO clipping ablation	Full GRPO	RGRA (no clipping) matches or exceeds	Neutral or positive

Key Takeaways

When designing RL post-training pipelines for LLMs, always include negative feedback for below-baseline actions—training only on successes significantly limits learning efficiency
PPO-style policy ratio clipping is not necessary for improving LLM reasoning; practitioners can safely simplify their training algorithms by removing it, reducing implementation complexity
REINFORCE-based approaches with group relative advantage are a viable, simpler alternative to GRPO for math reasoning fine-tuning, offering easier debugging, lower implementation overhead, and competitive performance

Abstract

Recent advances in large language models (LLMs) highlight the importance of post training techniques for improving reasoning and mathematical ability. Group Relative Policy Optimization (GRPO) has shown promise in this domain by combining group relative advantage estimation, PPO style clipping, and KL regularization. However, its complexity raises the question of whether all components are necessary for fostering reasoning behaviors. We conduct a systematic analysis of GRPO and identify two key findings: (1) incorporating negative feedback is essential training solely on actions above a baseline limits learning; and (2) PPO style constraints, such as policy ratio clipping, are not required to improve mathematical reasoning or performance. Building on these insights, we propose REINFORCE with Group Relative Advantage (RGRA), a simplified variant that retains group relative advantage estimation but removes PPO style clipping and policy ratio terms. Experiments across standard mathematical benchmarks indicate that RGRA has the potential to achieve stronger performance than GRPO. Our results suggest that simpler REINFORCE based approaches can effectively enhance reasoning in LLMs, offering a more transparent and efficient alternative to GRPO.