LLM Training & Reasoning
Supervised Reinforcement Learning: From Expert Trajectories to Step-wise Reasoning
This paper introduces Supervised Reinforcement Learning (SRL), a novel framework addressing the limitations of Supervised Fine-Tuning (SFT) and existing Reinforcement Learning with Verifiable Rewards (RLVR) in training Large Language Models (LLMs) for complex, multi-step reasoning tasks. SRL reformulates problem-solving as a sequential decision-making process, where the model generates an internal reasoning monologue before committing to an 'action'. It provides dense, step-wise rewards based on the similarity between the model's actions and expert actions, offering richer learning signals even when rollouts are incorrect. SRL enables small models to learn challenging problems previously intractable, generalizes to agentic software engineering tasks, and achieves highest performance when combined with RLVR.
Executive Impact & Key Metrics
Supervised Reinforcement Learning (SRL) provides dense, step-wise rewards based on sequence similarity between model-generated actions and expert actions, combined with an internal monologue. This contrasts with sparse, final-answer rewards of RLVR and rigid token-by-token imitation of SFT, enabling more flexible and effective learning for complex multi-step reasoning in LLMs.
3.0%
Average performance boost of SRL over baselines
3.7%
Additional performance gain when combining SRL with RLVR
14.8%
SRL resolve rate on SWE-Bench (Oracle File Edit)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Supervised Reinforcement Learning (SRL)
SRL reformulates problem-solving as a sequential decision-making process, where the model generates an internal reasoning monologue before committing to an 'action'. It provides dense, step-wise rewards based on the similarity between the model's actions and expert actions extracted from the SFT dataset. This offers richer learning signals and encourages flexible reasoning.
27.6%
Average performance (Avg@32) of SRL on Math Reasoning Benchmarks
SRL Training Process
Expert Trajectory Decomposition
→
Intermediate Action Extraction
→
Internal Monologue Generation
→
Action Prediction
→
Sequence Similarity Reward
→
Policy Optimization
| Feature | SFT | RLVR | SRL |
|---|---|---|---|
| Learning Signal | Token-level imitation (rigid) | Sparse (final answer correctness) | Dense, step-wise (action similarity) |
| Handling Incorrect Rollouts | N/A | Struggles (zero success) | Provides rich signals |
| Flexibility in Reasoning | Low (overfitting) | Can improve generalization (marginal) | High (interleaved planning/verification) |
| Performance on Hard Problems | Degradation | Marginal gains | Substantially outperforms |
Reinforcement Learning with Verifiable Rewards (RLVR)
RLVR optimizes models with reward signals based purely on the correctness of a final answer. While promising, it struggles when correct solutions are rarely sampled (sparse rewards), leading to uninformative policy gradients and training instability on challenging multi-step reasoning problems.
24.5%
Average performance (Avg@32) of RLVR on Math Reasoning Benchmarks
The Challenge of Sparse Rewards in RLVR
On challenging mathematical reasoning tasks (e.g., AMC, AIME), RLVR often fails because correct solutions are rarely sampled even after many attempts. An incorrect intermediate step can derail the entire reasoning chain, resulting in negative learning signals regardless of partially correct solutions. This makes these tasks largely intractable for standard outcome-based RL methods, highlighting the need for denser reward signals like those provided by SRL.
Supervised Fine-Tuning (SFT)
SFT uses expert demonstrations to train LLMs via a next-token prediction objective. While instilling valuable reasoning behaviors, its rigid, token-level imitation often limits generalization beyond training data, leading to overfitting and shallow reasoning, especially with modest or complex datasets. Performance degradation is often observed on challenging problems.
16.6%
Average performance (Avg@32) of SFT (R1 reasoning) on Math Reasoning Benchmarks
Calculate Your Potential AI ROI
Estimate the significant efficiency gains and cost savings your enterprise could achieve by integrating advanced AI reasoning capabilities.
Estimated Annual Savings
$-
Annual Hours Reclaimed
-
Implementation Roadmap
A phased approach to integrating Supervised Reinforcement Learning into your enterprise workflows.
Phase 1: Initial Assessment & Data Preparation
Identify critical reasoning tasks, gather expert demonstrations, and parse them into step-wise actions with internal monologues.
Phase 2: SRL Model Training & Iteration
Fine-tune base LLMs using the SRL framework with sequence similarity rewards and dynamic sampling on the prepared datasets.
Phase 3: RLVR Integration & Refinement
Optionally integrate SRL-trained models with RLVR for further performance enhancement, leveraging outcome-based rewards for final polish.
Phase 4: Deployment & Continuous Monitoring
Deploy the SRL-enhanced LLM agent and establish continuous monitoring for performance, adapting training as new data becomes available.
Ready to Transform Your Enterprise with AI?
Connect with our experts to discuss how Supervised Reinforcement Learning and other advanced AI strategies can drive significant improvements in your operations.
Ready to Get Started?
Book Your Free Consultation.
Let's Discuss Your AI Strategy!
Lets Discuss Your Needs
Select a Date
Sun
Mon
Tue
Wed
Thu
Fri
Sat