Reinforcement Learning is the Missing Key for Smarter LLMs
Feb 7, 2025
Reinforcement Learning (RL) has the potential to significantly enhance Large Language Models (LLMs) by enabling them to learn from their mistakes, improve their reasoning abilities, and optimize decisions dynamically.
Many of you asked me recently on my thoughts on Reinforcement Learning and its importance. We also have seen some great progress in this from OpenAI, DeepSeek and recently TWO AI. I tried to put together my thoughts on this here. Again, these is evolving field, and I am sure our understanding on this will evolve/change too. With that ...
Reinforcement Learning (RL) is one of the most underutilized techniques in the evolution of Large Language Models (LLMs). While supervised fine-tuning has been the industry standard for improving AI models post-training, it has fundamental limitations. Fine-tuning optimizes a model to follow learned patterns, but it doesn’t teach the model to think, reason, or self-correct. RL changes that by introducing an adaptive learning process where models don’t just generate outputs but learn from their mistakes, optimize decisions, and refine their reasoning abilities dynamically.
Beyond just Token Prediction: RL in Post-Training
At their core, LLMs operate as autoregressive models, predicting the next token based on learned probability distributions. This works well for fluent text generation but fails at structured reasoning, logical consistency, and decision optimization. RL enables models to move past this limitation by introducing a feedback-driven training loop where they actively improve based on predefined reward mechanisms.
One of the most immediate applications of RL in LLMs is hallucination control and logical coherence. Standard fine-tuned models often generate confidently incorrect answers because their training process only focuses on pattern replication, not truthfulness. RL allows us to build reward models that penalize factual inconsistencies and reinforce multi-step logical deduction. Instead of merely refining response style, RL can fundamentally alter the way LLMs structure their reasoning processes, leading to fewer hallucinations and improved contextual accuracy. (Note: we are not talking about RLHF, which we're kind of already using.un
The Role of RL in Multilingual Proficiency
Another major area where RL can revolutionize LLMs is multilingual proficiency. Indian languages, for instance, pose a challenge due to their morphological complexity and context-sensitive grammar. Direct fine-tuning helps with translation, but it doesn’t teach models to understand the deeper linguistic structure. RL, when combined with human-in-the-loop reinforcement or adaptive preference modeling, can refine multilingual accuracy beyond token-matching into true linguistic fluency.
For example, Hindi has gender-based noun variations that a naive translation model often misses:
“She is a teacher” → वह शिक्षिका है (feminine)
“He is a teacher” → वह शिक्षक है (masculine)
Similarly, in Gujarati we have formality-based verb shifts that a standard fine-tuned model will struggle with:
તું ક્યાં જઈ રહ્યો છે? (Informal: “Where are you going?”)
તમે ક્યાં જઈ રહ્યા છો? (Formal: “Where are you going?”)
A purely fine-tuned model might apply these rules inconsistently. But an RL-trained model, using adaptive reward conditioning, can continuously learn from real user interactions and adjust responses dynamically based on formality, context, and intent—making multilingual AI not just accurate, but truly usable in real-world scenarios.
RL for Stepwise Thinking: Enabling Structured Reasoning
One of the biggest weaknesses in LLMs today is their inability to perform multi-step logical deduction. Ask a model a simple arithmetic question like:
“If Rajiv has 10 mangoes and gives 3 to Seema, but Seema gives 2 back, how many does Rajiv have?”
A standard LLM might generate a quick answer without breaking down the logic, often leading to errors. This happens because the model doesn’t inherently reason—it only predicts patterns. RL introduces structured reward models that force stepwise reasoning, ensuring the AI breaks down the solution process instead of treating it as a single inference step. The difference is fundamental:
A naive model might answer directly, sometimes getting it wrong.
An RL-trained model would iteratively construct the logical flow:
Rajiv starts with 10 mangoes.
He gives 3 away → now has 7.
Seema returns 2 → now has 9.
This kind of structured reasoning is key not only for arithmetic but also for complex decision-making tasks where multi-step logical consistency is required. RL can be used to fine-tune chain-of-thought prompting, ensuring the model doesn’t just respond, but actually processes and derives answers in a structured manner.
SUTRA-R0: The Reinforcement-Learned Reasoning Model
At TWO AI, we are taking RL for LLMs a step further with SUTRA-R0, our first reinforcement-learning-powered reasoning model. Unlike traditional LLMs that stagnate after fine-tuning, SUTRA-R0 is designed to continuously evolve through adaptive reinforcement learning loops. It does not just memorize facts; it learns from its own outputs, optimizes multi-step reasoning, and refines decision-making in real-time.
By integrating reward-weighted inference mechanisms, adaptive linguistic optimization, and stepwise RL conditioning, SUTRA-R0 pushes the boundaries of what an LLM can achieve. It ensures that models don’t just generate grammatically correct text, but actively reason through problems, adapt to linguistic diversity, and optimize response accuracy over time.
The future of AI is not static models that memorize data—it’s systems that learn, adapt, and evolve continuously. Reinforcement Learning is the key to this transformation. With SUTRA-R0, we are building LLMs that don’t just respond—they think.
Original Article: https://www.linkedin.com/pulse/why-reinforcement-learning-missing-key-smarter-llms-pranav-mistry-z9v8c/?trackingId=%2BPCHMLzcR3e5xO10zhJJcw%3D%3D
Pranav
Recent Posts
